Estimation of Covid-19 lungs damage based on computer tomography images analysis

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The Covid-19 pandemic did accelerate development and research in several multidisciplinary areas. One of them is the use of software tools for faster and reproducible patient data evaluation. A CT scan can be invaluable for a search of details, but it is not always easy to see the big picture in 3D data. Even in the visual analysis of CT slice by slice can inter and intra variability makes a big difference. We present an ImageJ tool developed together with the radiology center of Faculty hospital Královské Vinohrady for CT evaluation of patients with COVID-19. The tool was developed to help estimate the percentage of lungs affected by the infection. The patients can be divided into five groups based on percentage score and proper treatment can be applied." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/11-326", "name": "Estimation of Covid-19 lungs damage based on computer tomography images..." } } ] } Home Browse Estimation of Covid-19 lungs damage based on computer tomography images... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Schätz M, Rubešová O, Mareš J et al. Estimation of Covid-19 lungs damage based on computer tomography images analysis [version 3; peer review: 2 approved, 1 not approved] . F1000Research 2025, 11 :326 ( https://doi.org/10.12688/f1000research.109020.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Software Tool Article Revised Estimation of Covid-19 lungs damage based on computer tomography images analysis [version 3; peer review: 2 approved, 1 not approved] Martin Schätz https://orcid.org/0000-0003-0931-4017 1 , Olga Rubešová https://orcid.org/0000-0003-2101-499X 1 , Jan Mareš 1 , David Girsa 2 , Alan Spark https://orcid.org/0000-0002-5112-4842 1 Martin Schätz https://orcid.org/0000-0003-0931-4017 1 , Olga Rubešová https://orcid.org/0000-0003-2101-499X 1 , [...] Jan Mareš 1 , David Girsa 2 , Alan Spark https://orcid.org/0000-0002-5112-4842 1 PUBLISHED 25 Jul 2025 Author details Author details 1 Department of Mathematics, Informatics and Cybernetics, University of Chemistry and Technology, Prague, 166 28, Czech Republic 2 Department of Radiodiagnostics 3FM CU and UHKV, Charles University 3rd Faculty of Medicine, Prague, 100 34, Czech Republic Martin Schätz Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Olga Rubešová Roles: Data Curation, Formal Analysis, Resources, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Jan Mareš Roles: Conceptualization, Funding Acquisition, Methodology, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing David Girsa Roles: Methodology, Resources Alan Spark Roles: Validation OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Coronavirus (COVID-19) collection. Abstract Modern treatment is based on reproducible quantitative analysis of available data. The Covid-19 pandemic did accelerate development and research in several multidisciplinary areas. One of them is the use of software tools for faster and reproducible patient data evaluation. A CT scan can be invaluable for a search of details, but it is not always easy to see the big picture in 3D data. Even in the visual analysis of CT slice by slice can inter and intra variability makes a big difference. We present an ImageJ tool developed together with the radiology center of Faculty hospital Královské Vinohrady for CT evaluation of patients with COVID-19. The tool was developed to help estimate the percentage of lungs affected by the infection. The patients can be divided into five groups based on percentage score and proper treatment can be applied. READ ALL READ LESS Keywords Computed Tomography, Image Analysis, ImageJ, Covid-19, Lungs Corresponding Author(s) Martin Schätz ( [email protected] ) Close Corresponding author: Martin Schätz Competing interests: No competing interests were disclosed. Grant information: The work was funded by the Ministry of Education, Youth and Sports by grant ‘Development of Advanced Computational Algorithms for evaluating post-surgery rehabilitation’ number LTAIN19007. The work was also supported from the grant of Specific university research – grant No FCHI 2022-001. Copyright: © 2025 Schätz M et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Schätz M, Rubešová O, Mareš J et al. Estimation of Covid-19 lungs damage based on computer tomography images analysis [version 3; peer review: 2 approved, 1 not approved] . F1000Research 2025, 11 :326 ( https://doi.org/10.12688/f1000research.109020.3 ) First published: 17 Mar 2022, 11 :326 ( https://doi.org/10.12688/f1000research.109020.1 ) Latest published: 25 Jul 2025, 11 :326 ( https://doi.org/10.12688/f1000research.109020.3 ) Revised Amendments from Version 2 We've refined the manuscript based on valuable reviewer feedback. Here's a summary of the key changes: Enhanced Variability Analysis You'll find a more robust analysis of user variability. We've now included both inter-user and intra-user variability analyses, providing a comprehensive view of consistency between different users and by a single user over time. Our approach was guided by the helpful reference: Popović ZB and Thomas JD "Assessing observer variability: a user’s guide" Cardiovasc Diagn Ther. 2017 Jun; 7(3): 317–324. The plugin, updated data and Jupyter Notebooks with analysis are available through the Zenodo repository or GitHub. The raw CT data are also available in a separate Zenodo repository. We've refined the manuscript based on valuable reviewer feedback. Here's a summary of the key changes: Enhanced Variability Analysis You'll find a more robust analysis of user variability. We've now included both inter-user and intra-user variability analyses, providing a comprehensive view of consistency between different users and by a single user over time. Our approach was guided by the helpful reference: Popović ZB and Thomas JD "Assessing observer variability: a user’s guide" Cardiovasc Diagn Ther. 2017 Jun; 7(3): 317–324. The plugin, updated data and Jupyter Notebooks with analysis are available through the Zenodo repository or GitHub. The raw CT data are also available in a separate Zenodo repository. See the authors' detailed response to the review by Alessandro Santini See the authors' detailed response to the review by Tamas Dolinay See the authors' detailed response to the review by Hamid A. Jalab READ REVIEWER RESPONSES Introduction The covid pandemic that has affected in recent months has revealed a number of strengths and weaknesses in health systems around the world. One of the key ideas is a quick and accurate diagnosis of the patient, which was problematic in congested hospitals. Software engineering and image processing methods could be helpful in speeding up and refining patient diagnosis, especially in radiological and radiodiagnostic workplaces, where a large part of diagnostic processes take place over image data (CT, NMR, X-ray). Recent advances in image analysis motivate for a more collaborative approach to quantitative analysis since it usually requires expertise in bioimage analysis. 1 , 2 Various software tools have been used for this purpose for years. In general, it is possible to divide them into two groups: • universal software packages: used for general analysis of image data such as filtering, smoothing or image registration • software tools “made to measure”: concrete software tools for analysis of rare diseases The first group of tools is represented mostly by software integrated into packages supplied by the tomograph developer. It is possible to mention a software tool for CT image preprocessing and automated analysis of three standard phantoms 3 or a software tool for reducing metal artifacts in dental care. 4 The second group of tools is from both the research and application point of view much more interesting. It is necessary to state that only a small part of them is applied in a real clinical environment. It is possible to mention a tool for analysis of GPA disease using image registration and self-organizing maps, 5 or a tool for analysis of peripheral bypass grafts. 6 Many research groups focused on precise measurement of pathological findings, 3D analysis, or volumetric analysis. 7 , 8 Moreover, some papers deal with image fusions from different scanners e.g. combination of data from CT, PET/CT, SPECT/CT, or MR. 9 , 10 Thus, the topic of CT image analysis of “covid lungs” is essential from both the research point of view (there is still room for further research in precise semi-automatic analysis) and the clinical point of view. The availability of tools for scientific research remains a challenge for both researchers and end-users. Although access to scientific papers is increasingly open, reproducible resources, code, and data availability is not yet widespread. Access to the results of scientific studies is crucial, but access to the necessary tools makes a real difference. Unfortunately, the code is not often available in open-source form, complete with step-by-step tutorials and opportunities for reporting issues. While software such as ImageJ 11 and 3D Slicer 12 exists for image analysis, they are geared toward experienced image analysts. They may not be user-friendly for end-users who are not familiar with creating analysis workflows. The end-user often depends on core facilities or available documentation and tutorials for support. The 3D Slicer CT Lungs Analyzer project for lung analysis is still in development and relies on Unet deep learning segmentation, but it is promising. There is a need for a portable, user-friendly software tool for reproducible quantitative analysis of CTs to estimate covid lung pneumonia. Therefore, the aim of software paper is to present a semi-automatic software for “covid lungs” CT image analysis, based on knowledge presented in Ref. 13 . The authors based the idea on the correlation between the degree of lung involvement and the course of the disease. The global score (0–25) of lung score involvement is calculated based on the extent of volume involvement (0: 0%, 1: 75%). The authors then introduce the role of CT score in predicting the outcome of SARS-CoV-2 patients. The scoring is highly correlated with laboratory findings, disease severity and mortality. Moreover, it might speed up diagnostic workflow in symptomatic cases. Methods Image format The Covid CT estimation tool is based on standard image processing techniques. Our interest is in volume, so the same voxel size is critical for good enough estimation. But it is also important to go through the different types of data we can encounter. In general, the Hounsfield Units (HU) make up the grayscale in medical CT imaging. It is a scale from black to white of 4096 values (12 bit) and ranges from -1024 HU to 3071 HU (zero is also a value). It is defined by the following: -1024 HU is black and represents air (in the lungs). 0 HU represents water (since we consist mostly out of the water, there is a large peak here). 3071 HU is white and represents the densest tissue in a human body, such as tooth enamel. Materials with higher atomic numbers, such as bones, appear as brighter areas on CT images and are assigned higher HU values (typically between +700 and +3000). All other tissues are somewhere within this scale; fat is around -100 HU, muscle around 100 HU, and bone spans from 200 HU (trabecular/spongeous bone) to about 2000 HU (cortical bone). DICOM files are usually saved in signed 16 bit, with original HU, usually with 3 mm slicing or 0.6 mm slicing CT images. TIFF, however, may have reshaped histogram values to cover the whole range and can preferably be in unsigned 16 bit or 8bit with some loss due to conversion. TIFF values usually lose Z voxel size metadata in conversion (resulting in Z voxel size value of 1), so it is essential to reset voxel values. The XY voxel size can be different with each data set, even from the same CT machine. The distribution of intensity values may change with different CT protocols, so some of the processing steps need to be done manually. Implementation The workflow follows the Croney Ethical guidelines for the appropriate use and manipulation of scientific digital images. 14 The plugin tool is developed in ImageJ macro language. It needs Bio Format plugin to import DICOM files, which comes installed in FIJI. The macro language uses standard image processing techniques and morphological operations to estimate the volume ratio of lungs and pneumonia caused by COVID-19. It allows users to subsequently set up a threshold for pneumonia and lungs, and go through the whole data-set slice by slice and interactively tweak the threshold values. The tool was developed based on demand and with coordination from the Department of Radiology from the Faculty hospital Královské Vinohrady. It is challenging to do any kind of percentage estimate of pneumonia in the lungs just by visually inspecting CT scans stack by stack. The available hardware equipment and local account restrictions had to be taken into account for development tool selection. The ImageJ plugin is a compromise in accuracy and requirements. The scripts are published with the paper. The workflow for 8-bit script version is following: 1) Input and pre-processing a) Clear the log and close all open images. b) Print the version of ImageJ and the Bio-Formats Macro Extensions being used. If untested version of ImageJ is being used, a warning message is displayed. c) Get the user's input for the type of image file to be processed (TIF, DICOM, Siemens DICOM, or Compressed DICOM) and the directory where the images are stored. d) Open the selected image files from the input directory. e) Get the dimensions of the image stack (width, height, channels, slices, and frames). f) Get the user's input on the start and end slices of the lung region. g) Duplicate the stack of slices selected for the lung region. h) Apply a median filter to the stack of lung slices. i) Enhance the contrast of the stack of lung slices. (Only 8 bit version). j) Duplicate the stack of lung slices twice, creating two separate stacks for lung thresholding and pneumonia thresholding. 2) Analysis a) For the stack of lung slices, convert the image to 8-bit and apply a threshold to remove all but the lung tissue. b) Lungs i). Get the user's input on the threshold values for the lung tissue. ii). Convert the thresholded image to a mask and clean the mask using erode, dilate, and fill holes operation. iii). Analyze the selection in the mask to separate the individual lung regions in each stack. iv). Save the processed image as a TIF file in a new directory with the date and time as part of the file name. c) Pneumonia i). Get the user's input on the threshold values for pneumonia. ii). Convert the thresholded image to a mask and clean the mask using erode and dilate. iii). Combine the mask with the lung mask using an AND operation. iv). Analyze the particles in the mask. v). Save the processed image as a TIF file in a new directory with the date and time as part of the file name. 3) Evaluation a) Create a new image with CT data as channel 1, lung mask as channel 2 and pneumonia mask as channel 3. Save the composite as TIF in the results folder. b) Get the total area of lungs and total area of pneumonia for all stacks. c) Evaluate percentage of pneumonia area in lungs, and score the results using (0:0%; 1, 75%; range 0–5) function. d) Save log containing information about the whole process in results folder. Numeric result and composition image representation from step 3.a (original data, lung and pneumonia mask) is shown to the user (as illustrated in Figure 1 ). Figure 1. Result of analysis as RGB stack, where Red channel contains CT data, Green channel lung mask and Blue channel pneumonia mask. Numeric results in percent are corrected by subtracting 3% (median of tissue present in healthy lungs, estimated from 10 patients) and CT is scored based on severity ranged (0:0%; 1, 75%; range 0–5) defined by Ref. 13 . Operation There are several steps during the tool runtime which require user inputs: 1. Select the CT lung data ( Figure 2 , TIFF or DICOM file based on script version) - CT sequence is opened and user can go through loaded stack in image sequence with a slider or as a video with a play button. 2. “Please find the start of lungs in stack” - user has an option to select the first image with lungs with a slider and confirm the selection with “Ok” button. 3. “Please find the end of lungs in stack” - user has an option to select the last image of lungs selection with the slider and confirm with “Ok” button. The tool works with the images only in between the chosen interval of the lungs stack to minimize the computational effort. 4. “Setup threshold for all but body” - the whole image- exclude the body, shall be highlighted with red colour. The tool makes automatic estimation, and the user can adjust the threshold with the sliders on the histogram. Confirm with the “Ok” button. 5. “Setup threshold of Covid” - the covid threshold shall be highlighted with red colour. The tool makes automatic estimation, and the user can adjust the threshold with the sliders on the histogram. It is not a problem if part of the body (not lungs!) will be chosen together with Covid. The tool automatically subtracts the body threshold from the chosen Covid threshold. Confirm with the “Ok” button. • After each calculation the tool adds information to the log window. The log file is automatically saved to the CT data directory. The output lungs and covid masks are saved in TIFF format into an additional folder in the CT data location. • The tool provides % estimation of Covid damage in the lungs and a semi-quantitative CT score. The score is calculated based on the extent of lobar involvement (0:0%; 1, 75%; range 0–5 based on the medical research “Chest CT score in COVID-19 patients: correlation with the disease severity and short-term prognosis. 13 Figure 2. ImageJ Tool, loading data options. The tool has been tested both 3 mm slicing and 0.6 mm slicing CT images. The results were similar in percentage and the final CT score was the same. In order to use the tool, the user needs to prepare CT images exported as DICOM or TIFF in the preferred view mode and preferably 16-bit representation. The CT images usually have a 12-bit gray-scale representation and an 8-bit conversion would lead to loss of potentially important information or shift of brightness values. The thickness of the CT slice can also contribute to numerical errors in the process, but there was no significant difference in results when processing the same data-set with 3 mm and 0.6 slicing. The ImageJ software tool available from Zenodo or GitHub needs an ImageJ (ideally version 1.52v99 or newer) installed with Bio-Formats (preferably with version 6.8.0 which we tested) plugin (or FIJI which is a version of ImageJ with an already integrated Bio-Formats plugin). The minimal requirements for both are Windows XP or later with Java installed, Mac OS X 10.8 or later with Java installed, Ubuntu Linux 12.04 LTS, or later with Java installed. Minimal RAM is based on the size of processed images. In this case, multiple images are opened at once. Use cases The usability of the introduced tools is presented in the next sections. A use case for comparison for a CT measured with different slicing setup is presented. Results for a set of 5 CTs evaluated by different users is discussed. Since we were restricted by hardware, two versions of tool were created. One that works with 8-bit version of images and needs less RAM, and one that works with 16-bit signed images and can load HU units. The CT scans of COVID-19 patients used in this section were provided by the Department of Radiology of Faculty hospital Královské Vinohrady, where the tool was tested and deployed in September 2021. Slice thickness variation The international standard for saving DICOM files defines 3 mm slicing of CT data as the default way. However resaving data as TIFF (losing voxel information) or using different slice thicknesses (like 0.6 mm slicing) may result in a different result. In theory, 0.6 slicing would provide 5 times more detailed sampling in the Z-axis. However, in practice it is different. The same CT dataset exported with 0.6 and 3 mm slices (XZ view for comparison is in Figure 3 ) was analyzed with our tool with a lung threshold of 0-155 and a pneumonia threshold of 47-115. The results can be found in Table 1 . The error from a comparison of 3 mm and 0.6 mm slicing is estimated at 0.58 %. The used CT is available in the attached published dataset as CT1_1 (0.6 mm slicing) and CT1_2 (3 mm slicing). Figure 3. XZ view comparison of 3 mm and 0.6 mm CT. Table 1. Comparison of results from 0.6 mm and 3 mm 8-bit dataset. Slicing Lungs slices Lungs threshold Pneumoina threshold Percentage Scoring 0.6 mm 60-505 0-155 47-115 31.21 3 3 mm 12-101 0-155 47-115 31.79 3 User inter and intra variability The biggest challenge in using this tool is an individual perception of images, as each person may see image data fundamentally the same - despite different appearances. Based on this a user can add the biggest bias even though the underlying data analysis is done correctly. The Table 2 contains a comparison of the results of the analysis in on 5 different CT datasets provided by the Faculty hospital of Královské Vinohrady. All CTs are analysed by users with different experience. The first CT exported with different slicing (also used in Table 1 ) is analysed by a radiologist (an expert user). The ANOVA test ( Table 5 Results for Score, Table 6 Results for Percentage) assesses the variance between datasets is statistically significant ( For Score F-value: 75.06 and p-value: 1.64e-21 , For Percentage F-value: 89.85 and p-value: 3.53e-23 ), proving the selection of datasets is representative for comparisons. The score aims to divide the percentage into groups based on previous research done, 13 and should be the deciding factor for future care for patients. Table 2. Independent analysis results using the 8bit version of tool, example logs available at github.com/martinschatz-cz/ImageJ_Pneumonia_Estimation_Tool . Dataset Slicing Radiologist Rad. score User 1 User 1 score User 2 User 2 score User 3 User 3 score CT1_1 0.6 mm 31% 3 50% 3 30% 3 30% 3 CT1_2 3.0 mm 32% 3 55% 4 33% 3 45% 3 CT2 3.0 mm - - 10% 2 5% 1 7% 1 CT3 0.6 mm - - 41% 3 24% 2 42% 4 CT4 3.0 mm - - 64% 4 64% 4 64% 4 CT5 3.0 mm - - 2% 1 3% 1 4% 1 Table 3. Scoring results for 3 repetitions. Run 1 Run 2 Run 3 Dataset Score Percentage Dataset Score Percentage Dataset Score Percentage CT1_1 3.0 32.11 CT3 3.0 30.00 CT1_1 2.0 23.12 CT1_1 3.0 42.14 CT2 2.0 20.72 CT1_1 3.0 50.68 CT2 2.0 18.15 CT5 1.0 1.06 CT1_2 4.0 59.63 CT4 4.0 74.76 CT1_1 3.0 32.84 CT2 1.0 4.62 CT3 3.0 37.70 CT3 3.0 42.95 CT3 3.0 43.24 CT1_1 3.0 32.83 CT1_1 3.0 45.15 CT4 4.0 69.70 CT2 2.0 11.79 CT4 4.0 72.60 CT3 3.0 30.62 CT5 1.0 2.84 CT1_2 3.0 36.31 CT2 2.0 9.56 CT4 4.0 71.12 CT5 1.0 1.23 CT4 4.0 69.18 CT3 3.0 28.11 CT1_1 3.0 48.35 CT1_2 3.0 52.55 CT4 4.0 64.64 CT2 2.0 15.45 CT2 1.0 5.14 CT3 3.0 34.76 CT4 4.0 71.02 CT4 4.0 70.17 CT5 1.0 2.21 CT2 2.0 19.45 CT5 1.0 0.14 CT2 2.0 19.59 CT1_2 2.0 25.45 CT1_1 3.0 47.73 CT5 1.0 1.62 CT3 3.0 35.46 CT5 1.0 0.48 CT1_2 3.0 49.25 CT1_2 2.0 22.72 CT3 3.0 33.72 CT1_2 3.0 42.38 CT5 1.0 2.94 CT5 1.0 0.52 CT1_2 3.0 51.98 CT4 4.0 65.34 CT1_2 4.0 55.97 Table 4. Standard Deviation and CV for Each Metric. Filename mean std cv metric CT1_1 2.888889 0.333333 0.115385 Score CT1_2 3.000000 0.707107 0.235702 Score CT2 1.777778 0.440959 0.248039 Score CT3 3.000000 0.000000 0.000000 Score CT4 4.000000 0.000000 0.000000 Score CT5 1.000000 0.000000 0.000000 Score CT1_1 39.438889 9.497811 0.240823 Percentage CT1_2 44.026667 13.278828 0.301609 Percentage CT2 13.830000 6.287444 0.454624 Percentage CT3 35.173333 5.381494 0.152999 Percentage CT4 69.836667 3.215369 0.046041 Percentage CT5 1.448889 1.029629 0.710634 Percentage Figure 4. Comparison of CT scoring and threshold in between users. More details and code is available at https://github.com/martinschatz-cz/ImageJ_Pneumonia_Estimation_Tool . (a) Distribution of CT scoring from three users, Table 2 . (b) Distribution of lower threshold over three users. (c) Distribution of upper threshold over three users. Ensuring the reliability and accuracy of results when working with user-input tools is crucial and requires careful consideration of inter- and intra-variability. This challenge can be addressed by using standardized procedures and guidelines, multiple raters for segmentation, and computer-aided methods. Our software tool addresses this issue by providing standardized procedures and guidelines, along with the ability to compare results through logs and promote reproducibility. It is essential to take into account the level of training and experience of individuals performing the segmentation, as well as the time and resources available, as these factors can significantly impact the consistency and accuracy of the segmentation. The software tool provides a solution for addressing the challenges of inter and intra variability in CT data segmentation, helping to ensure careful planning and execution of a study and appropriate outputs to achieve repeatable and comparable results. Using scoring will overcome some of the problems of comparing percentages directly. Figure (Comparison of CT scoring) shows that users rely on their experience and will choose parameters based on them. It shows the difficulties in ensuring consistency and accuracy of data segmentation when performed manually by multiple individuals. The possibility of comparing the results of the analysis of multiple users using a defined analysis process ( Figure 4a ) leads to more reliable results. CT1_1 and CT1_2 is the same dataset with different slicing and percentage results of analysis from all users clearly show inter-variability ( Table 2 , thresholds and scores in Figure 4 ). The overall scoring is the same as the result from the trained radiologists (CT1_1 - 31%, score 3; CT1_2 - 32%, score 3). Intra variability 15 was established by 3 runs of set of randomized and blinded datasets. Each run consisted of 3 copies of the 6 published datasets, in random order. There was a at least 4 weeks in between of evaluation of each run. The resulting Statistical Analysis consists of the standard deviation and coefficient of variation for each metric (percentage and score) across repetition and an ANOVA ( Table 5 Results for Score and Table 6 Results for Percentage) and intraclass correlation coefficient (ICC) analysis to assess agreement and consistency. The results can be found in Table 3 Scoring results for 3 repetitions. Tool performance based on descriptive stats The single-user scoring system demonstrates excellent reliability (ICC1 = 0.91), showing that the user's scoring using proposed toll is consistent across datasets. This reliability is critical since all dataset evaluations depend on the judgment of one rater using this tool. When we average scores across multiple evaluations (by the same user), the reliability improves even further (ICC1k = 0.97). This result indicates that averaging can mitigate occasional inconsistencies and ensure stable, reproducible scoring. The high ICC values confirm that the scoring process is robust and reliable, even when performed by a single evaluator. This ensures the validity of the results and confidence in the conclusions drawn from the data analysis. The full ICC results are in Table 7 Results for Score and Table 8 Results for Percentage. The tool still demonstrates varying precision across datasets ( Table 4 Standard Deviation and CV for Each Metric). Lower cv values for Percentage in CT4_TIFF and consistent Score in CT3_TIFF (cv: 0.00) suggest high reliability for these cases. Showing that previously designed Scoring parameters are well based, and preferred for use, while percentage can be valuable but not as reliable information. Because of this, user training, and using provided guidance will lead to stable results. Table 5. ANOVA Results for Score. Source ddof1 ddof2 F p-unc np2 Filename 5 48 75.062069 1.639097e-21 0.886608 Table 6. ANOVA Results for Percentage. Source ddof1 ddof2 F p-unc np2 Filename 5 48 89.852298 3.532717e-23 0.903471 Table 7. ICC Results for Score. Type Description ICC F df1 df2 pval CI95% ICC1 Single raters absolute 0.911795 32.011765 5 12 0.000002 [0.71, 0.99] ICC2 Single random raters 0.911366 27.484848 5 10 0.000015 [0.69, 0.99] ICC3 Single fixed raters 0.898253 27.484848 5 10 0.000015 [0.65, 0.98] ICC1k Average raters absolute 0.968761 32.011765 5 12 0.000002 [0.88, 1.0] ICC2k Average random raters 0.968600 27.484848 5 10 0.000015 [0.87, 1.0] ICC3k Average fixed raters 0.963616 27.484848 5 10 0.000015 [0.85, 0.99] Table 8. ICC Results for Percentage. Type Description ICC F df1 df2 pval CI95% ICC1 Single raters absolute 0.927952 39.639008 5 12 4.708720e-07 [0.75, 0.99] ICC2 Single random raters 0.927662 33.969098 5 10 5.806596e-06 [0.74, 0.99] ICC3 Single fixed raters 0.916595 33.969098 5 10 5.806596e-06 [0.7, 0.99] ICC1k Average raters absolute 0.974772 39.639008 5 12 4.708720e-07 [0.9, 1.0] ICC2k Average random raters 0.974666 33.969098 5 10 5.806596e-06 [0.89, 1.0] ICC3k Average fixed raters 0.970561 33.969098 5 10 5.806596e-06 [0.88, 1.0] Discussion The ImageJ/FIJI tool can import various DICOM or TIFF files. Users should be always aware of whenever the saved data are using signed or unsigned bit depth, as unsigned data will shift pixel brightness. The same will happen when exporting data in different bit depth or with a specific CT view. The slicing of the CT dataset also matters, however, the analysis in Table 1 showed that it won’t significantly affect either the percentage or the score (other CT machines might have different settings). A small case study for user inter and intra variability was made ( Table 2 ) to evaluate the usability of the proposed tool. Some expected variability in results occurs, interesting is inter variability in evaluating CT1 which is 3-5%. The intra variability is more extensive, up to 20%, and points out the fact that users should have at least some training in how to recognize pneumonia in CT images. Conclusions The tool was developed on demand from the Department of Radiology at the Faculty hospital Královské Vinohrady, as it was difficult for them to estimate the percentage and score of pneumonia in the lungs just by visually inspecting CT scans. Available hardware equipment and local account restrictions had to be taken into account for development tool selection. The ImageJ plugin is a compromise in accuracy and requirements. It logs all the user inputs for reproducibility and saves the results of all the steps as TIFF stacks. These masks and images can be used for visual inspection or possibly in the future for more advanced machine learning tools. This software tool is the first step of a longer journey to create a tool that would be both easy to use for radiologists to diagnose COVID-19 based on CTs and include an advanced image analysis tool for percentage estimation of pneumonia in lungs. The use of open software promises ease of future development, however, it might be beneficial to move from ImageJ to 3D Slicer 12 or Napari 16 as they offer better tools for 3D visualization and integration of machine learning tools, which we aim to develop and integrate into our future works. Limitations The biggest limitation of this approach is human error and inter and intra variation of manual selection. The percentage estimation might also be affected by other body cavities filled with air. There might also be a variance in results based on slice thickness, in worst case scenario 20%, but our experiment shows that there is only about 0.58% difference in result between 0.6 and 3 mm CT slice thickness. The scoring should also be improved so it is not dependent only on one value (volume percentage), but normalized SHU distribution in the pneumonia area should be also considered. When converting from 12-bit to 8-bit image representation, the reduced range of values results in a loss of information and detail, which can lower the quality of the output. However, for CT image segmentation, the use of Single Hounsfield Unit (SHU) values is adequate, as SHUs do not rely on single units and can provide good-quality segmentation. For evaluating the agreement between repeated measurements, we used the intraclass correlation coefficient (ICC) . ICC is a widely used indicator of reliability in medical research. 15 However, it is known that the ICC value is sensitive to the range of variability in the measured sample . 15 A narrow range of true values in the sample can lead to a low ICC even with good measurement agreement, and conversely, a wide range can artificially inflate the ICC value. Therefore, when interpreting our ICC results, it is necessary to consider the potential influence of data range . Our decision to use ICC was motivated by its common use for evaluating inter- and intra-rater reliability and the desire for comparability with existing literature in the field of image data analysis. From the software point of view, there is a limitation in the version of ImageJ used. The new version of the code logs the ImageJ version and BioImage plugin version. There is a version of the code explicitly made for ImageJ version 1.52v99 and for other versions. The bind version helps reproducibility of any analysis based on logs, and it is advised to reproduce the analysis in the same version of ImageJ as indicated in logs. Data availability Underlying data Zenodo: CT scans of COVID-19 patients, https://doi.org/10.5281/zenodo.5805939 . 17 Datasets contain CT scans of COVID-19 patients from Faculty hospital of Královké Vinohrady in DICOM (and TIFF), as per the folder name. Dataset CT1 is presented with 0.6 mm and 3 mm slicing. This project contains the following underlying data: • CT1_1 – CT1_1_TIFF_06_MM (Single stack 8-bit TIFF data) • CT1_2 – CT1_2_TIFF_3_MM (Single stack 8-bit TIFF data) • CT2 – CT2_DICOM – CT2_TIFF (Single stack 8-bit TIFF data) • CT3 – CT3_DICOM – CT3_TIFF (Single stack 8-bit TIFF data) • CT4 – CT4_DICOM – CT4_TIFF (Single stack 8-bit TIFF data) • CT5 – CT5_DICOM – CT5_TIFF (Single stack 8-bit TIFF data) • results_csv.csv Data are available under the terms of the Creative Commons Attribution 4.0 International (CC-BY 4.0). Software availability Zenodo: ImageJ tool for percentage estimation of pneumonia in lungs, https://doi.org/10.5281/zenodo.15827771 . 18 The third version of the repository contains both a new version of ImageJ scripts (.ijm files in folder tools ) and ImageJ scripts published with the first version of this Software Tools article (subfolder 0.3c1 of folder tools). The new folders inter _intra and repeatability contains the source files and Jupyter Notebook files used for the evaluation and the presented graphs. The repository is accompanied by overview of project, limitations and step-by-step tutorial using the CT3 dataset published as a part of the underlying data. All this information is also available on GitHub repository: https://github.com/martinschatz-cz/ImageJ_Pneumonia_Estimation_Tool . This supplementary material aims to improve reproducibility and with the community approved way to publish workflows. 19 This project structure describes the crucial underlying data: • Inter_intra ○ Inter_variability ▪ user_eval.ipynb ▪ users_result.csv ○ Intra_variability ▪ intra_var_analysis.ipynb ▪ Results_1.csv ▪ Results_2.csv ▪ Results_3.csv • repeatability ○ score_eval ○ time_eval • tools ○ SEQUENCE_Est_Percentage_CT_16bit_V04_IJ_152v99.ijm ○ SEQUENCE_Est_Percentage_CT_u8bit_V04_IJ_152v99.ijm ○ SEQUENCE_Est_Percentage_CT_u8bit_V04.ijm ○ 0.3c1 ▪ SEQUENCE_Est_Percentage_CT_16bit_V03_clean.ijm (16bit version) ▪ SEQUENCE_Est_Percentage_CT_u8bit_V03_clean.ijm (8bit version) • README.md Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Acknowledgements Computational resources were supplied by the project “e-Infrastruktura CZ” (e-INFRA LM2018140) provided within the program Projects of Large Research, Development and Innovations Infrastructures. Special acknowledgment goes to the Department of Radiology of Faculty hospital Královské Vinohrady, who provided the data, for medical support. References 1. Levet F, Carpenter AE, Eliceiri KW, et al. : Developing open-source software for bioimage analysis: opportunities and challenges. F1000Res. April 2021; 10 : 302. PubMed Abstract | Publisher Full Text | Free Full Text 2. Schlaeppi A, Adams W, Haase R, et al. : Meeting in the middle: Towards successful multidisciplinary bioimage analysis collaboration. Front. Bioinform. 2022 Apr; 2 : 889755. PubMed Abstract | Publisher Full Text | Free Full Text 3. Torfeh T, Beaumont S, Guédon J, et al. : Software tools dedicated for an automatic analysis of the ct scanner quality control’s images. 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PubMed Abstract | Publisher Full Text | Free Full Text Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 17 Mar 2022 ADD YOUR COMMENT Comment Author details Author details 1 Department of Mathematics, Informatics and Cybernetics, University of Chemistry and Technology, Prague, 166 28, Czech Republic 2 Department of Radiodiagnostics 3FM CU and UHKV, Charles University 3rd Faculty of Medicine, Prague, 100 34, Czech Republic Martin Schätz Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Olga Rubešová Roles: Data Curation, Formal Analysis, Resources, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Jan Mareš Roles: Conceptualization, Funding Acquisition, Methodology, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing David Girsa Roles: Methodology, Resources Alan Spark Roles: Validation Competing interests No competing interests were disclosed. Grant information The work was funded by the Ministry of Education, Youth and Sports by grant ‘Development of Advanced Computational Algorithms for evaluating post-surgery rehabilitation’ number LTAIN19007. The work was also supported from the grant of Specific university research – grant No FCHI 2022-001. Article Versions (3) version 3 Revised Published: 25 Jul 2025, 11:326 https://doi.org/10.12688/f1000research.109020.3 version 2 Revised Published: 03 Jul 2023, 11:326 https://doi.org/10.12688/f1000research.109020.2 version 1 Published: 17 Mar 2022, 11:326 https://doi.org/10.12688/f1000research.109020.1 Copyright © 2025 Schätz M et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Schätz M, Rubešová O, Mareš J et al. Estimation of Covid-19 lungs damage based on computer tomography images analysis [version 3; peer review: 2 approved, 1 not approved] . F1000Research 2025, 11 :326 ( https://doi.org/10.12688/f1000research.109020.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 3 VERSION 3 PUBLISHED 25 Jul 2025 Revised Views 0 Cite How to cite this report: Santini A. Reviewer Report For: Estimation of Covid-19 lungs damage based on computer tomography images analysis [version 3; peer review: 2 approved, 1 not approved] . F1000Research 2025, 11 :326 ( https://doi.org/10.5256/f1000research.184052.r400406 ) The direct URL for this report is: https://f1000research.com/articles/11-326/v3#referee-response-400406 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 19 Aug 2025 Alessandro Santini , Department of Biomedical Sciences, Humanitas University, Milan, Italy Approved VIEWS 0 https://doi.org/10.5256/f1000research.184052.r400406 I thank the Authors for their review and update of ... Continue reading READ ALL I thank the Authors for their review and update of the manuscript, which I feel is now suitable for indexing. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Acute respiratory distress syndrome, ventilator-induced lung injury, critical care medicine I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Santini A. Reviewer Report For: Estimation of Covid-19 lungs damage based on computer tomography images analysis [version 3; peer review: 2 approved, 1 not approved] . F1000Research 2025, 11 :326 ( https://doi.org/10.5256/f1000research.184052.r400406 ) The direct URL for this report is: https://f1000research.com/articles/11-326/v3#referee-response-400406 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 2 VERSION 2 PUBLISHED 03 Jul 2023 Revised Views 0 Cite How to cite this report: Santini A. Reviewer Report For: Estimation of Covid-19 lungs damage based on computer tomography images analysis [version 3; peer review: 2 approved, 1 not approved] . F1000Research 2025, 11 :326 ( https://doi.org/10.5256/f1000research.147768.r211856 ) The direct URL for this report is: https://f1000research.com/articles/11-326/v2#referee-response-211856 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 01 Nov 2023 Alessandro Santini , Department of Biomedical Sciences, Humanitas University, Milan, Italy Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.147768.r211856 The Authors present a new tool for quantitative analysis of CT scans of COVID-19 patients. The novelty of this tool, compared to already available softwares such as 3D Slicer, is its availability (the code is open-source) and the alleged ease ... Continue reading READ ALL The Authors present a new tool for quantitative analysis of CT scans of COVID-19 patients. The novelty of this tool, compared to already available softwares such as 3D Slicer, is its availability (the code is open-source) and the alleged ease of use even by non-experienced users. However, when presenting results of inter- and intra-user variability of results, the Authors show not very promising results. They also acknowledge this problem in the discussion: "The biggest challenge in using this tool is an individual perception of images (...) Based on this a user can add the biggest bias even though the underlying data analysis is done correctly". While I agree with the Authors, I do not see how this problem is overcome by their tool. The Authors later in the manuscript state that "this challenge can be addressed by using standardized procedures and guidelines, multiple raters for segmentation, and computer-aided methods". However, it is not clear from this paper that this challenge has efficiently being addressed/solved. This is not a secondary issue for a tool which is designed to be "user-friendly" and easy to use even by a non-experienced user. I suggest the Authors to expand their inter- and intra-user variability analysis. While inter-user variability is presented, I did not find any data on intra-user variability (same user performing the analysis on a single CT more than once). You can use as a guide the following ref: Popović ZB and Thomas JD "Assessing observer variability: a user’s guide" Cardiovasc Diagn Ther. 2017 Jun; 7(3): 317–324. Furthermore, as the tool is supposed to give the same results as other, already available, softwares, the Authors should present a comparison between the results obtained with their tool and the results obtained with other(s) software(s) on their CT scans. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Yes Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Partly Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Partly References 1. Popović ZB, Thomas JD: Assessing observer variability: a user's guide. Cardiovasc Diagn Ther . 2017; 7 (3): 317-324 PubMed Abstract | Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Acute respiratory distress syndrome, ventilator-induced lung injury, critical care medicine I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Santini A. Reviewer Report For: Estimation of Covid-19 lungs damage based on computer tomography images analysis [version 3; peer review: 2 approved, 1 not approved] . F1000Research 2025, 11 :326 ( https://doi.org/10.5256/f1000research.147768.r211856 ) The direct URL for this report is: https://f1000research.com/articles/11-326/v2#referee-response-211856 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 30 Nov 2023 Martin Schätz , Department of Mathematics, Informatics and Cybernetics, University of Chemistry and Technology, Prague, 166 28, Czech Republic 30 Nov 2023 Author Response Dear Dr. Alessandro Santini, we express our sincere gratitude for your diligent review of our manuscript and for providing constructive and insightful comments. We wish to address your ... Continue reading Dear Dr. Alessandro Santini, we express our sincere gratitude for your diligent review of our manuscript and for providing constructive and insightful comments. We wish to address your suggestion for the comparison of our tool with other software tools first. Numerous freeware programs exist for visualizing scientific image data, such as Imaris Viewer ( https://imaris.oxinst.com/imaris-viewer ), ZEISS ZEN lite ( https://www.zeiss.com/microscopy/en/products/software/zeiss-zen-lite.html ), and open-source software like 3D Slicer and FIJI/ImageJ, which offer manual BioImage Analysis and script creation capabilities. Analogous to CT software, these applications typically afford scientific data visualization, with open-source variants featuring additional tools or plugins for specific and systematic analyses. While we appreciate the suggestion to compare our tool with the Slicer Lung CT Analyzer extension of 3D Slicer, it is important to note that, as of the current date (21.11.2023), the extension is still in development and remains unpublished (you can find details on their GitHub repository: https://github.com/rbumm/SlicerLungCTAnalyzer ). We eagerly anticipate the extension's publication and look forward to a thorough comparison, as their design is sound, and offers a good alternative in case of access to the proper hardware. Publishing software tools takes time and when Slicer Lung CT Analyzer extension is published, it deserves a proper citation and acknowledgment. We value your feedback regarding the improvement of the use case and the suggestion to expand the paper more. To clarify our aim for user-friendliness, the tool was developed with the goal in mind, that „guessing“ volumetric percentage out of CT viewer is not an exact quantitative analysis. While manual analysis in other software is possible after user training, our tool's systematic approach, documented parameters, and step-by-step guide enable hospital staff to apply it with reasonable variability. During the tool's pilot implementation at FNKH Faculty Hospital, our focus was on lowering inter-user variability, as reflected in our report. However, what was tested was a score comparison between CT of the same patient with different slice thicknesses (CT1_1 with 0.6mm slices and CT1_2 with 3 mm slices). It was originally designed to assess score variability with different slice thicknesses. This comparison also provides insight into intra-user variability; however, we acknowledge that the number of repetitions may have some limitations. Once again, we sincerely appreciate your invaluable feedback. Best regards, Martin Schätz Dear Dr. Alessandro Santini, we express our sincere gratitude for your diligent review of our manuscript and for providing constructive and insightful comments. We wish to address your suggestion for the comparison of our tool with other software tools first. Numerous freeware programs exist for visualizing scientific image data, such as Imaris Viewer ( https://imaris.oxinst.com/imaris-viewer ), ZEISS ZEN lite ( https://www.zeiss.com/microscopy/en/products/software/zeiss-zen-lite.html ), and open-source software like 3D Slicer and FIJI/ImageJ, which offer manual BioImage Analysis and script creation capabilities. Analogous to CT software, these applications typically afford scientific data visualization, with open-source variants featuring additional tools or plugins for specific and systematic analyses. While we appreciate the suggestion to compare our tool with the Slicer Lung CT Analyzer extension of 3D Slicer, it is important to note that, as of the current date (21.11.2023), the extension is still in development and remains unpublished (you can find details on their GitHub repository: https://github.com/rbumm/SlicerLungCTAnalyzer ). We eagerly anticipate the extension's publication and look forward to a thorough comparison, as their design is sound, and offers a good alternative in case of access to the proper hardware. Publishing software tools takes time and when Slicer Lung CT Analyzer extension is published, it deserves a proper citation and acknowledgment. We value your feedback regarding the improvement of the use case and the suggestion to expand the paper more. To clarify our aim for user-friendliness, the tool was developed with the goal in mind, that „guessing“ volumetric percentage out of CT viewer is not an exact quantitative analysis. While manual analysis in other software is possible after user training, our tool's systematic approach, documented parameters, and step-by-step guide enable hospital staff to apply it with reasonable variability. During the tool's pilot implementation at FNKH Faculty Hospital, our focus was on lowering inter-user variability, as reflected in our report. However, what was tested was a score comparison between CT of the same patient with different slice thicknesses (CT1_1 with 0.6mm slices and CT1_2 with 3 mm slices). It was originally designed to assess score variability with different slice thicknesses. This comparison also provides insight into intra-user variability; however, we acknowledge that the number of repetitions may have some limitations. Once again, we sincerely appreciate your invaluable feedback. Best regards, Martin Schätz Competing Interests: No competing interests were disclosed. Close Report a concern Author Response 09 Aug 2025 Martin Schätz , Department of Mathematics, Informatics and Cybernetics, University of Chemistry and Technology, Prague, 166 28, Czech Republic 09 Aug 2025 Author Response Dear reviewer, thank you very much for insightfull feedback and recomendations to improve our analysis of our tool. Addressing User Bias and Tool's Focus We agree that user perception ... Continue reading Dear reviewer, thank you very much for insightfull feedback and recomendations to improve our analysis of our tool. Addressing User Bias and Tool's Focus We agree that user perception introduces inherent variability in manual segmentation, a challenge common to all semi-automatic tools. Our tool addresses this not by eliminating user bias entirely, but by simplifying the segmentation process, making it more accessible and reducing errors from completely manual focus often used in hospitals. Its primary focus is on providing a standardized framework for quantitative analysis. This approach, coupled with full reproducibility through detailed logging of all sub-results, is particularly beneficial in a hospital setting where multiple raters often compare results. This was our key motivation for initially focusing on inter-user variability, as a single rater typically doesn't repeat the analysis on the same scan in clinical practice. Intra- and Inter-User Variability Analysis We appreciate the suggestion to expand our variability analysis. We've now included both intra-user and inter-user variability analyses in the updated manuscript. Jupyter notebooks with analysis, data and subresults are included in Zenodo and also available on GitHub, which is more user friendly for browsing such files. We would like to thank you for the helpful reference (Popović ZB and Thomas JD "Assessing observer variability: a user’s guide" Cardiovasc Diagn Ther. 2017 Jun; 7(3): 317–324), which guided our approach and is cited in the paper. Comparison with Existing Software Our tool is specifically developed for quantitative analysis in a hospital environment. This setting is often strict about software installation and may lack the necessary hardware to run complex neural network or deep learning models efficiently , if at all. While we acknowledge the importance of comparing our results with other published software, the specific 3D Slicer plugin relevant to our approach is still under development, as noted in its GitHub repository ( https://github.com/Slicer/SlicerLungCTAnalyzer ). We plan to conduct a thorough comparison with this and potentially other tools as they become finalized and published. At the moment the creators of this tool recomends/references general CT neural network segmenters which are useful for lung segmentation or segmentation of anatomic structures from CT. Unfortunately, that is only substep in whole analysis workflow. We agree the comparison is a crucial point for future versions and further analysis of our tool. Dear reviewer, thank you very much for insightfull feedback and recomendations to improve our analysis of our tool. Addressing User Bias and Tool's Focus We agree that user perception introduces inherent variability in manual segmentation, a challenge common to all semi-automatic tools. Our tool addresses this not by eliminating user bias entirely, but by simplifying the segmentation process, making it more accessible and reducing errors from completely manual focus often used in hospitals. Its primary focus is on providing a standardized framework for quantitative analysis. This approach, coupled with full reproducibility through detailed logging of all sub-results, is particularly beneficial in a hospital setting where multiple raters often compare results. This was our key motivation for initially focusing on inter-user variability, as a single rater typically doesn't repeat the analysis on the same scan in clinical practice. Intra- and Inter-User Variability Analysis We appreciate the suggestion to expand our variability analysis. We've now included both intra-user and inter-user variability analyses in the updated manuscript. Jupyter notebooks with analysis, data and subresults are included in Zenodo and also available on GitHub, which is more user friendly for browsing such files. We would like to thank you for the helpful reference (Popović ZB and Thomas JD "Assessing observer variability: a user’s guide" Cardiovasc Diagn Ther. 2017 Jun; 7(3): 317–324), which guided our approach and is cited in the paper. Comparison with Existing Software Our tool is specifically developed for quantitative analysis in a hospital environment. This setting is often strict about software installation and may lack the necessary hardware to run complex neural network or deep learning models efficiently , if at all. While we acknowledge the importance of comparing our results with other published software, the specific 3D Slicer plugin relevant to our approach is still under development, as noted in its GitHub repository ( https://github.com/Slicer/SlicerLungCTAnalyzer ). We plan to conduct a thorough comparison with this and potentially other tools as they become finalized and published. At the moment the creators of this tool recomends/references general CT neural network segmenters which are useful for lung segmentation or segmentation of anatomic structures from CT. Unfortunately, that is only substep in whole analysis workflow. We agree the comparison is a crucial point for future versions and further analysis of our tool. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 30 Nov 2023 Martin Schätz , Department of Mathematics, Informatics and Cybernetics, University of Chemistry and Technology, Prague, 166 28, Czech Republic 30 Nov 2023 Author Response Dear Dr. Alessandro Santini, we express our sincere gratitude for your diligent review of our manuscript and for providing constructive and insightful comments. We wish to address your ... Continue reading Dear Dr. Alessandro Santini, we express our sincere gratitude for your diligent review of our manuscript and for providing constructive and insightful comments. We wish to address your suggestion for the comparison of our tool with other software tools first. Numerous freeware programs exist for visualizing scientific image data, such as Imaris Viewer ( https://imaris.oxinst.com/imaris-viewer ), ZEISS ZEN lite ( https://www.zeiss.com/microscopy/en/products/software/zeiss-zen-lite.html ), and open-source software like 3D Slicer and FIJI/ImageJ, which offer manual BioImage Analysis and script creation capabilities. Analogous to CT software, these applications typically afford scientific data visualization, with open-source variants featuring additional tools or plugins for specific and systematic analyses. While we appreciate the suggestion to compare our tool with the Slicer Lung CT Analyzer extension of 3D Slicer, it is important to note that, as of the current date (21.11.2023), the extension is still in development and remains unpublished (you can find details on their GitHub repository: https://github.com/rbumm/SlicerLungCTAnalyzer ). We eagerly anticipate the extension's publication and look forward to a thorough comparison, as their design is sound, and offers a good alternative in case of access to the proper hardware. Publishing software tools takes time and when Slicer Lung CT Analyzer extension is published, it deserves a proper citation and acknowledgment. We value your feedback regarding the improvement of the use case and the suggestion to expand the paper more. To clarify our aim for user-friendliness, the tool was developed with the goal in mind, that „guessing“ volumetric percentage out of CT viewer is not an exact quantitative analysis. While manual analysis in other software is possible after user training, our tool's systematic approach, documented parameters, and step-by-step guide enable hospital staff to apply it with reasonable variability. During the tool's pilot implementation at FNKH Faculty Hospital, our focus was on lowering inter-user variability, as reflected in our report. However, what was tested was a score comparison between CT of the same patient with different slice thicknesses (CT1_1 with 0.6mm slices and CT1_2 with 3 mm slices). It was originally designed to assess score variability with different slice thicknesses. This comparison also provides insight into intra-user variability; however, we acknowledge that the number of repetitions may have some limitations. Once again, we sincerely appreciate your invaluable feedback. Best regards, Martin Schätz Dear Dr. Alessandro Santini, we express our sincere gratitude for your diligent review of our manuscript and for providing constructive and insightful comments. We wish to address your suggestion for the comparison of our tool with other software tools first. Numerous freeware programs exist for visualizing scientific image data, such as Imaris Viewer ( https://imaris.oxinst.com/imaris-viewer ), ZEISS ZEN lite ( https://www.zeiss.com/microscopy/en/products/software/zeiss-zen-lite.html ), and open-source software like 3D Slicer and FIJI/ImageJ, which offer manual BioImage Analysis and script creation capabilities. Analogous to CT software, these applications typically afford scientific data visualization, with open-source variants featuring additional tools or plugins for specific and systematic analyses. While we appreciate the suggestion to compare our tool with the Slicer Lung CT Analyzer extension of 3D Slicer, it is important to note that, as of the current date (21.11.2023), the extension is still in development and remains unpublished (you can find details on their GitHub repository: https://github.com/rbumm/SlicerLungCTAnalyzer ). We eagerly anticipate the extension's publication and look forward to a thorough comparison, as their design is sound, and offers a good alternative in case of access to the proper hardware. Publishing software tools takes time and when Slicer Lung CT Analyzer extension is published, it deserves a proper citation and acknowledgment. We value your feedback regarding the improvement of the use case and the suggestion to expand the paper more. To clarify our aim for user-friendliness, the tool was developed with the goal in mind, that „guessing“ volumetric percentage out of CT viewer is not an exact quantitative analysis. While manual analysis in other software is possible after user training, our tool's systematic approach, documented parameters, and step-by-step guide enable hospital staff to apply it with reasonable variability. During the tool's pilot implementation at FNKH Faculty Hospital, our focus was on lowering inter-user variability, as reflected in our report. However, what was tested was a score comparison between CT of the same patient with different slice thicknesses (CT1_1 with 0.6mm slices and CT1_2 with 3 mm slices). It was originally designed to assess score variability with different slice thicknesses. This comparison also provides insight into intra-user variability; however, we acknowledge that the number of repetitions may have some limitations. Once again, we sincerely appreciate your invaluable feedback. Best regards, Martin Schätz Competing Interests: No competing interests were disclosed. Close Report a concern Author Response 09 Aug 2025 Martin Schätz , Department of Mathematics, Informatics and Cybernetics, University of Chemistry and Technology, Prague, 166 28, Czech Republic 09 Aug 2025 Author Response Dear reviewer, thank you very much for insightfull feedback and recomendations to improve our analysis of our tool. Addressing User Bias and Tool's Focus We agree that user perception ... Continue reading Dear reviewer, thank you very much for insightfull feedback and recomendations to improve our analysis of our tool. Addressing User Bias and Tool's Focus We agree that user perception introduces inherent variability in manual segmentation, a challenge common to all semi-automatic tools. Our tool addresses this not by eliminating user bias entirely, but by simplifying the segmentation process, making it more accessible and reducing errors from completely manual focus often used in hospitals. Its primary focus is on providing a standardized framework for quantitative analysis. This approach, coupled with full reproducibility through detailed logging of all sub-results, is particularly beneficial in a hospital setting where multiple raters often compare results. This was our key motivation for initially focusing on inter-user variability, as a single rater typically doesn't repeat the analysis on the same scan in clinical practice. Intra- and Inter-User Variability Analysis We appreciate the suggestion to expand our variability analysis. We've now included both intra-user and inter-user variability analyses in the updated manuscript. Jupyter notebooks with analysis, data and subresults are included in Zenodo and also available on GitHub, which is more user friendly for browsing such files. We would like to thank you for the helpful reference (Popović ZB and Thomas JD "Assessing observer variability: a user’s guide" Cardiovasc Diagn Ther. 2017 Jun; 7(3): 317–324), which guided our approach and is cited in the paper. Comparison with Existing Software Our tool is specifically developed for quantitative analysis in a hospital environment. This setting is often strict about software installation and may lack the necessary hardware to run complex neural network or deep learning models efficiently , if at all. While we acknowledge the importance of comparing our results with other published software, the specific 3D Slicer plugin relevant to our approach is still under development, as noted in its GitHub repository ( https://github.com/Slicer/SlicerLungCTAnalyzer ). We plan to conduct a thorough comparison with this and potentially other tools as they become finalized and published. At the moment the creators of this tool recomends/references general CT neural network segmenters which are useful for lung segmentation or segmentation of anatomic structures from CT. Unfortunately, that is only substep in whole analysis workflow. We agree the comparison is a crucial point for future versions and further analysis of our tool. Dear reviewer, thank you very much for insightfull feedback and recomendations to improve our analysis of our tool. Addressing User Bias and Tool's Focus We agree that user perception introduces inherent variability in manual segmentation, a challenge common to all semi-automatic tools. Our tool addresses this not by eliminating user bias entirely, but by simplifying the segmentation process, making it more accessible and reducing errors from completely manual focus often used in hospitals. Its primary focus is on providing a standardized framework for quantitative analysis. This approach, coupled with full reproducibility through detailed logging of all sub-results, is particularly beneficial in a hospital setting where multiple raters often compare results. This was our key motivation for initially focusing on inter-user variability, as a single rater typically doesn't repeat the analysis on the same scan in clinical practice. Intra- and Inter-User Variability Analysis We appreciate the suggestion to expand our variability analysis. We've now included both intra-user and inter-user variability analyses in the updated manuscript. Jupyter notebooks with analysis, data and subresults are included in Zenodo and also available on GitHub, which is more user friendly for browsing such files. We would like to thank you for the helpful reference (Popović ZB and Thomas JD "Assessing observer variability: a user’s guide" Cardiovasc Diagn Ther. 2017 Jun; 7(3): 317–324), which guided our approach and is cited in the paper. Comparison with Existing Software Our tool is specifically developed for quantitative analysis in a hospital environment. This setting is often strict about software installation and may lack the necessary hardware to run complex neural network or deep learning models efficiently , if at all. While we acknowledge the importance of comparing our results with other published software, the specific 3D Slicer plugin relevant to our approach is still under development, as noted in its GitHub repository ( https://github.com/Slicer/SlicerLungCTAnalyzer ). We plan to conduct a thorough comparison with this and potentially other tools as they become finalized and published. At the moment the creators of this tool recomends/references general CT neural network segmenters which are useful for lung segmentation or segmentation of anatomic structures from CT. Unfortunately, that is only substep in whole analysis workflow. We agree the comparison is a crucial point for future versions and further analysis of our tool. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Dolinay T. Reviewer Report For: Estimation of Covid-19 lungs damage based on computer tomography images analysis [version 3; peer review: 2 approved, 1 not approved] . F1000Research 2025, 11 :326 ( https://doi.org/10.5256/f1000research.147768.r183705 ) The direct URL for this report is: https://f1000research.com/articles/11-326/v2#referee-response-183705 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 18 Jul 2023 Tamas Dolinay , Department of Medicine, University of California, Los Angeles, Los Angeles, CA, USA Approved VIEWS 0 https://doi.org/10.5256/f1000research.147768.r183705 The authors have sufficiently revised the manuscript. I believe the revised paper is ... Continue reading READ ALL The authors have sufficiently revised the manuscript. I believe the revised paper is an important addition to the growing field of CT imaging in COVID-19 lung disease. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Pulmonology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Dolinay T. Reviewer Report For: Estimation of Covid-19 lungs damage based on computer tomography images analysis [version 3; peer review: 2 approved, 1 not approved] . F1000Research 2025, 11 :326 ( https://doi.org/10.5256/f1000research.147768.r183705 ) The direct URL for this report is: https://f1000research.com/articles/11-326/v2#referee-response-183705 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 17 Mar 2022 Views 0 Cite How to cite this report: Jalab HA. Reviewer Report For: Estimation of Covid-19 lungs damage based on computer tomography images analysis [version 3; peer review: 2 approved, 1 not approved] . F1000Research 2025, 11 :326 ( https://doi.org/10.5256/f1000research.120473.r153263 ) The direct URL for this report is: https://f1000research.com/articles/11-326/v1#referee-response-153263 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 25 Oct 2022 Hamid A. Jalab , [email protected] Hamid A. Jalab Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.120473.r153263 The study describes a novel image tool developed for CT assessment of COVID-19 patients. The tool was created to assist in estimating the percentage of lungs infected by the virus. The manuscript is interesting and addresses an ... Continue reading READ ALL The study describes a novel image tool developed for CT assessment of COVID-19 patients. The tool was created to assist in estimating the percentage of lungs infected by the virus. The manuscript is interesting and addresses an important issue. Major comments: 1. The Introduction should provide a strong argument for why the software tool is important. 2. It is of importance to have sufficient results to justify the novelty of the proposed software tool. 3. The robustness of the proposed software tool has not been addressed; this should be emphasized in the discussion section. 4. What are the benefits of the proposed approach above other current the new software tool? Is the rationale for developing the new software tool clearly explained? No Is the description of the software tool technically sound? No Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? No Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? No Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? No Competing Interests: No competing interests were disclosed. Reviewer Expertise: image processing I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Jalab HA. Reviewer Report For: Estimation of Covid-19 lungs damage based on computer tomography images analysis [version 3; peer review: 2 approved, 1 not approved] . F1000Research 2025, 11 :326 ( https://doi.org/10.5256/f1000research.120473.r153263 ) The direct URL for this report is: https://f1000research.com/articles/11-326/v1#referee-response-153263 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 03 Jul 2023 Martin Schätz , Department of Computing and Control Engineering, University of Chemistry and Technology, Prague, 166 28, Czech Republic 03 Jul 2023 Author Response Dear Dr. Hamid A. Jalab, I am extremely grateful for the time and effort you have dedicated to reviewing our Software Tool Article. Your constructive comments have been instrumental ... Continue reading Dear Dr. Hamid A. Jalab, I am extremely grateful for the time and effort you have dedicated to reviewing our Software Tool Article. Your constructive comments have been instrumental in shaping our manuscript, and I truly appreciate your contributions to the peer review process. Our replies to your comments: 1. The Introduction should provide a strong argument for why the software tool is important. Thank you for your valuable feedback. We agree that the Introduction should provide a strong argument for the software tool's importance. As we highlighted in the new paragraph, there is a need for a open source, user-friendly software tool for reproducible quantitative analysis of CT scans to estimate COVID lung pneumonia. Current software tools such as ImageJ and 3D Slicer may not be user-friendly for end-users who are not familiar with creating analysis workflows. Our software tool aims to fill this gap by providing a user-friendly solution for reproducible quantitative analysis, with available code, training data, tutorial and GitHub repository. 2. It is of importance to have sufficient results to justify the novelty of the proposed software tool. The main motivation for the software tool was that no other similar software tool was available for use on the computer infrastructure of Faculty Hospital of Královské Vinohrady. The main obstacles were in internal network policy, the unavailability of any high performance computing hardware, and need of specific tool. Since more hospitals might be challenged in similar ways, the highest motivation was to share our work openly and freely to help innovate and enable them. 3. The robustness of the proposed software tool has not been addressed; this should be emphasized in the discussion section. Since the software tool is user operated, there is high inter and intra variability of results – which we addressed in text. The robustness of whole workflow is now addressed in terms of repeatability. 4. What are the benefits of the proposed approach above other current the new software tool? The only other open-source software tool adressing similar medical analysis is plugin SlicerLungCTAnalyzer for 3D Slicer software (available on GitHub: https://github.com/rbumm/SlicerLungCTAnalyzer). Of course there also exists commercially available software like Thoracic VCAR and others, but the money and hardware requirements might render these software unreachable for a lot of facilities, and since the tools are closed it might be enough for diagnosis but not for other research. The benefits of our Software tool are that it is developed open-source, in its very low hardware requirements and its ease of use with low technical requirements for installation. These were also the requirements of the faculty hospital where it was requested. Is the rationale for developing the new software tool clearly explained? We added a comparison to other freely available open source software tool as requested. Is the description of the software tool technically sound? We improved the pseudo-code on top of the available source code, which should improve the description overview of the whole tool. Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? The whole original code and its update is available through a linked (now updated) repository and on GitHub with a small tutorial. The data and software availability parts were extended. We added pseudocode for a better overview, but best option is to go through the code. Any encountered problems with the macro on specific machines and/or ImageJ version can be reported through an issue on GitHub repository. Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? The output of the software tool is a log of whole process and partial results, it is a necessary step for reproducibility, good data management and interpretation. If you encountered any issues with the tool or prepared protocols please report them in GitHub repositories so we can properly address them. We are happy to help, sustainability is important part of our project. Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? We included more analysis about performance of the tool and also inter and intra variability overview (also available on GitHub). Scripts are included in linked materials with both new version of Software Tool. In the meantime we did optimization and some correction of reported issues of our ImageJ scripts, you are able to find them in the linked GitHub repository. It involves time and result-robustness tests , inter and intra variance overview from reported parameters used, a new version including updated log protocols to help users troubleshoot possible issues with the version of ImageJ. New logs from both 8 and 16-bit versions of scripts now contain both the ImageJ version used and Bio-Image version used. We will welcome any issue reports or enhancement suggestions in the GitHub repository. Thank you again for your valuable feedback. Martin Schätz Dear Dr. Hamid A. Jalab, I am extremely grateful for the time and effort you have dedicated to reviewing our Software Tool Article. Your constructive comments have been instrumental in shaping our manuscript, and I truly appreciate your contributions to the peer review process. Our replies to your comments: 1. The Introduction should provide a strong argument for why the software tool is important. Thank you for your valuable feedback. We agree that the Introduction should provide a strong argument for the software tool's importance. As we highlighted in the new paragraph, there is a need for a open source, user-friendly software tool for reproducible quantitative analysis of CT scans to estimate COVID lung pneumonia. Current software tools such as ImageJ and 3D Slicer may not be user-friendly for end-users who are not familiar with creating analysis workflows. Our software tool aims to fill this gap by providing a user-friendly solution for reproducible quantitative analysis, with available code, training data, tutorial and GitHub repository. 2. It is of importance to have sufficient results to justify the novelty of the proposed software tool. The main motivation for the software tool was that no other similar software tool was available for use on the computer infrastructure of Faculty Hospital of Královské Vinohrady. The main obstacles were in internal network policy, the unavailability of any high performance computing hardware, and need of specific tool. Since more hospitals might be challenged in similar ways, the highest motivation was to share our work openly and freely to help innovate and enable them. 3. The robustness of the proposed software tool has not been addressed; this should be emphasized in the discussion section. Since the software tool is user operated, there is high inter and intra variability of results – which we addressed in text. The robustness of whole workflow is now addressed in terms of repeatability. 4. What are the benefits of the proposed approach above other current the new software tool? The only other open-source software tool adressing similar medical analysis is plugin SlicerLungCTAnalyzer for 3D Slicer software (available on GitHub: https://github.com/rbumm/SlicerLungCTAnalyzer). Of course there also exists commercially available software like Thoracic VCAR and others, but the money and hardware requirements might render these software unreachable for a lot of facilities, and since the tools are closed it might be enough for diagnosis but not for other research. The benefits of our Software tool are that it is developed open-source, in its very low hardware requirements and its ease of use with low technical requirements for installation. These were also the requirements of the faculty hospital where it was requested. Is the rationale for developing the new software tool clearly explained? We added a comparison to other freely available open source software tool as requested. Is the description of the software tool technically sound? We improved the pseudo-code on top of the available source code, which should improve the description overview of the whole tool. Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? The whole original code and its update is available through a linked (now updated) repository and on GitHub with a small tutorial. The data and software availability parts were extended. We added pseudocode for a better overview, but best option is to go through the code. Any encountered problems with the macro on specific machines and/or ImageJ version can be reported through an issue on GitHub repository. Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? The output of the software tool is a log of whole process and partial results, it is a necessary step for reproducibility, good data management and interpretation. If you encountered any issues with the tool or prepared protocols please report them in GitHub repositories so we can properly address them. We are happy to help, sustainability is important part of our project. Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? We included more analysis about performance of the tool and also inter and intra variability overview (also available on GitHub). Scripts are included in linked materials with both new version of Software Tool. In the meantime we did optimization and some correction of reported issues of our ImageJ scripts, you are able to find them in the linked GitHub repository. It involves time and result-robustness tests , inter and intra variance overview from reported parameters used, a new version including updated log protocols to help users troubleshoot possible issues with the version of ImageJ. New logs from both 8 and 16-bit versions of scripts now contain both the ImageJ version used and Bio-Image version used. We will welcome any issue reports or enhancement suggestions in the GitHub repository. Thank you again for your valuable feedback. Martin Schätz Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 03 Jul 2023 Martin Schätz , Department of Computing and Control Engineering, University of Chemistry and Technology, Prague, 166 28, Czech Republic 03 Jul 2023 Author Response Dear Dr. Hamid A. Jalab, I am extremely grateful for the time and effort you have dedicated to reviewing our Software Tool Article. Your constructive comments have been instrumental ... Continue reading Dear Dr. Hamid A. Jalab, I am extremely grateful for the time and effort you have dedicated to reviewing our Software Tool Article. Your constructive comments have been instrumental in shaping our manuscript, and I truly appreciate your contributions to the peer review process. Our replies to your comments: 1. The Introduction should provide a strong argument for why the software tool is important. Thank you for your valuable feedback. We agree that the Introduction should provide a strong argument for the software tool's importance. As we highlighted in the new paragraph, there is a need for a open source, user-friendly software tool for reproducible quantitative analysis of CT scans to estimate COVID lung pneumonia. Current software tools such as ImageJ and 3D Slicer may not be user-friendly for end-users who are not familiar with creating analysis workflows. Our software tool aims to fill this gap by providing a user-friendly solution for reproducible quantitative analysis, with available code, training data, tutorial and GitHub repository. 2. It is of importance to have sufficient results to justify the novelty of the proposed software tool. The main motivation for the software tool was that no other similar software tool was available for use on the computer infrastructure of Faculty Hospital of Královské Vinohrady. The main obstacles were in internal network policy, the unavailability of any high performance computing hardware, and need of specific tool. Since more hospitals might be challenged in similar ways, the highest motivation was to share our work openly and freely to help innovate and enable them. 3. The robustness of the proposed software tool has not been addressed; this should be emphasized in the discussion section. Since the software tool is user operated, there is high inter and intra variability of results – which we addressed in text. The robustness of whole workflow is now addressed in terms of repeatability. 4. What are the benefits of the proposed approach above other current the new software tool? The only other open-source software tool adressing similar medical analysis is plugin SlicerLungCTAnalyzer for 3D Slicer software (available on GitHub: https://github.com/rbumm/SlicerLungCTAnalyzer). Of course there also exists commercially available software like Thoracic VCAR and others, but the money and hardware requirements might render these software unreachable for a lot of facilities, and since the tools are closed it might be enough for diagnosis but not for other research. The benefits of our Software tool are that it is developed open-source, in its very low hardware requirements and its ease of use with low technical requirements for installation. These were also the requirements of the faculty hospital where it was requested. Is the rationale for developing the new software tool clearly explained? We added a comparison to other freely available open source software tool as requested. Is the description of the software tool technically sound? We improved the pseudo-code on top of the available source code, which should improve the description overview of the whole tool. Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? The whole original code and its update is available through a linked (now updated) repository and on GitHub with a small tutorial. The data and software availability parts were extended. We added pseudocode for a better overview, but best option is to go through the code. Any encountered problems with the macro on specific machines and/or ImageJ version can be reported through an issue on GitHub repository. Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? The output of the software tool is a log of whole process and partial results, it is a necessary step for reproducibility, good data management and interpretation. If you encountered any issues with the tool or prepared protocols please report them in GitHub repositories so we can properly address them. We are happy to help, sustainability is important part of our project. Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? We included more analysis about performance of the tool and also inter and intra variability overview (also available on GitHub). Scripts are included in linked materials with both new version of Software Tool. In the meantime we did optimization and some correction of reported issues of our ImageJ scripts, you are able to find them in the linked GitHub repository. It involves time and result-robustness tests , inter and intra variance overview from reported parameters used, a new version including updated log protocols to help users troubleshoot possible issues with the version of ImageJ. New logs from both 8 and 16-bit versions of scripts now contain both the ImageJ version used and Bio-Image version used. We will welcome any issue reports or enhancement suggestions in the GitHub repository. Thank you again for your valuable feedback. Martin Schätz Dear Dr. Hamid A. Jalab, I am extremely grateful for the time and effort you have dedicated to reviewing our Software Tool Article. Your constructive comments have been instrumental in shaping our manuscript, and I truly appreciate your contributions to the peer review process. Our replies to your comments: 1. The Introduction should provide a strong argument for why the software tool is important. Thank you for your valuable feedback. We agree that the Introduction should provide a strong argument for the software tool's importance. As we highlighted in the new paragraph, there is a need for a open source, user-friendly software tool for reproducible quantitative analysis of CT scans to estimate COVID lung pneumonia. Current software tools such as ImageJ and 3D Slicer may not be user-friendly for end-users who are not familiar with creating analysis workflows. Our software tool aims to fill this gap by providing a user-friendly solution for reproducible quantitative analysis, with available code, training data, tutorial and GitHub repository. 2. It is of importance to have sufficient results to justify the novelty of the proposed software tool. The main motivation for the software tool was that no other similar software tool was available for use on the computer infrastructure of Faculty Hospital of Královské Vinohrady. The main obstacles were in internal network policy, the unavailability of any high performance computing hardware, and need of specific tool. Since more hospitals might be challenged in similar ways, the highest motivation was to share our work openly and freely to help innovate and enable them. 3. The robustness of the proposed software tool has not been addressed; this should be emphasized in the discussion section. Since the software tool is user operated, there is high inter and intra variability of results – which we addressed in text. The robustness of whole workflow is now addressed in terms of repeatability. 4. What are the benefits of the proposed approach above other current the new software tool? The only other open-source software tool adressing similar medical analysis is plugin SlicerLungCTAnalyzer for 3D Slicer software (available on GitHub: https://github.com/rbumm/SlicerLungCTAnalyzer). Of course there also exists commercially available software like Thoracic VCAR and others, but the money and hardware requirements might render these software unreachable for a lot of facilities, and since the tools are closed it might be enough for diagnosis but not for other research. The benefits of our Software tool are that it is developed open-source, in its very low hardware requirements and its ease of use with low technical requirements for installation. These were also the requirements of the faculty hospital where it was requested. Is the rationale for developing the new software tool clearly explained? We added a comparison to other freely available open source software tool as requested. Is the description of the software tool technically sound? We improved the pseudo-code on top of the available source code, which should improve the description overview of the whole tool. Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? The whole original code and its update is available through a linked (now updated) repository and on GitHub with a small tutorial. The data and software availability parts were extended. We added pseudocode for a better overview, but best option is to go through the code. Any encountered problems with the macro on specific machines and/or ImageJ version can be reported through an issue on GitHub repository. Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? The output of the software tool is a log of whole process and partial results, it is a necessary step for reproducibility, good data management and interpretation. If you encountered any issues with the tool or prepared protocols please report them in GitHub repositories so we can properly address them. We are happy to help, sustainability is important part of our project. Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? We included more analysis about performance of the tool and also inter and intra variability overview (also available on GitHub). Scripts are included in linked materials with both new version of Software Tool. In the meantime we did optimization and some correction of reported issues of our ImageJ scripts, you are able to find them in the linked GitHub repository. It involves time and result-robustness tests , inter and intra variance overview from reported parameters used, a new version including updated log protocols to help users troubleshoot possible issues with the version of ImageJ. New logs from both 8 and 16-bit versions of scripts now contain both the ImageJ version used and Bio-Image version used. We will welcome any issue reports or enhancement suggestions in the GitHub repository. Thank you again for your valuable feedback. Martin Schätz Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Dolinay T. Reviewer Report For: Estimation of Covid-19 lungs damage based on computer tomography images analysis [version 3; peer review: 2 approved, 1 not approved] . F1000Research 2025, 11 :326 ( https://doi.org/10.5256/f1000research.120473.r146294 ) The direct URL for this report is: https://f1000research.com/articles/11-326/v1#referee-response-146294 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 10 Aug 2022 Tamas Dolinay , Department of Medicine, University of California, Los Angeles, Los Angeles, CA, USA Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.120473.r146294 Dr. Schätz and colleagues describe a new image analysis modality to quickly review and score lung CT scan images. The image analysis was applied to patients with COVID-19 pneumonia to aid with diagnostic accuracy. It is increasingly recognized that viral ... Continue reading READ ALL Dr. Schätz and colleagues describe a new image analysis modality to quickly review and score lung CT scan images. The image analysis was applied to patients with COVID-19 pneumonia to aid with diagnostic accuracy. It is increasingly recognized that viral pneumonias, including COVID-19-associated pneumonia, may have a specific radiographic pattern. Automated modalities, including the one described by the authors can help with the rapid identification viral pneumonias and with measurement of the extent of the disease. The manuscript is interesting and covers an important topic, but its current format suffers from a few weaknesses. Major comments: The abstract describes 5 different pattern of radiographic findings, but I do not find these in the manuscript. A novelty of the article would be the stratification of pneumonias based on the imaging. Please describe, if you had success implementing the image analysis in your clinical practice. What are the benefits and/or barriers of using the analysis in the real world? Minor comments: Please write out the abbreviation when used for the first time to help the reader, who is not necessarily familiar with the specific language used. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Yes Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Pulmonology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Dolinay T. Reviewer Report For: Estimation of Covid-19 lungs damage based on computer tomography images analysis [version 3; peer review: 2 approved, 1 not approved] . F1000Research 2025, 11 :326 ( https://doi.org/10.5256/f1000research.120473.r146294 ) The direct URL for this report is: https://f1000research.com/articles/11-326/v1#referee-response-146294 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 03 Jul 2023 Martin Schätz , Department of Computing and Control Engineering, University of Chemistry and Technology, Prague, 166 28, Czech Republic 03 Jul 2023 Author Response Dear Dr. Tamas Dolinay, I want to express my sincere gratitude for taking the time to review our work and providing us with such thoughtful feedback. Your comments have helped ... Continue reading Dear Dr. Tamas Dolinay, I want to express my sincere gratitude for taking the time to review our work and providing us with such thoughtful feedback. Your comments have helped us to strengthen our work significantly, and I appreciate your efforts. Our replies to your major comments: 1) The abstract describes 5 different pattern of radiographic findings, but I do not find these in the manuscript. A novelty of the article would be the stratification of pneumonia based on the imaging. We appreciate your comments and would like to address your concern about the radiographic findings described in the abstract. The 5 groups are distinguished based on the percentage coverage in the lungs. We appologize for the confusion. We will revise the abstract to more accurately reflect your comment. The 5 different scores are originally based on paper „Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis“ (https://doi.org/10.1007/s00330-020-07033-y) where the lungs are divided and the result of each part is summed up to the score. It would be possible to do the same with 3D Slicer and the „Lung CT Analyzer“ (https://github.com/rbumm/SlicerLungCTAnalyzer) deep learning tool, however, it would require installing it and having more powerful hardware available. The software tool paper aims to present software tool that was made per request of Faculty Hospital, however analysis of different pattern of radiographic findings would be quite an interesting suggestion for a full research article. 2) Please describe, if you had success implementing the image analysis in your clinical practice. What are the benefits and/or barriers of using the analysis in the real world? The ImageJ software tool was developed on request by the Facultypital of Královské Vinohrady, and it has been used by the Radiology center since September 2021. The score was mainly used for additional estimation to help doctors estimate the severity of COVID-19 disease in hospitalized patients. The first benefit is having a quick estimation of lung coverage by disease, which can be challenging to estimate by visual inspection. The second benefit is that the ImageJ and this specific SW Tool do not need installation (which can be challenging at the hospital IT infrastructure) and works on a average office computer. The challenge is mentioned inter and intra variability per user, but logging the whole process helps with that. In meantime we did optimization and some correction of reported issues of our ImageJ scripts, you are able to find them in the linked GitHub repository. It involves time and result robustness tests , inter and intra variance overview from reported parameters used, a new version including updated log protocols to help users troubleshoot possible issues with the version of ImageJ. New logs from both 8 and 16-bit versions of scripts now contain both the ImageJ version used and Bio-Image version used. We will welcome any issue reports or enhancement suggestions in the GitHub repository. Thank you again for your valuable feedback. Martin Schätz Dear Dr. Tamas Dolinay, I want to express my sincere gratitude for taking the time to review our work and providing us with such thoughtful feedback. Your comments have helped us to strengthen our work significantly, and I appreciate your efforts. Our replies to your major comments: 1) The abstract describes 5 different pattern of radiographic findings, but I do not find these in the manuscript. A novelty of the article would be the stratification of pneumonia based on the imaging. We appreciate your comments and would like to address your concern about the radiographic findings described in the abstract. The 5 groups are distinguished based on the percentage coverage in the lungs. We appologize for the confusion. We will revise the abstract to more accurately reflect your comment. The 5 different scores are originally based on paper „Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis“ (https://doi.org/10.1007/s00330-020-07033-y) where the lungs are divided and the result of each part is summed up to the score. It would be possible to do the same with 3D Slicer and the „Lung CT Analyzer“ (https://github.com/rbumm/SlicerLungCTAnalyzer) deep learning tool, however, it would require installing it and having more powerful hardware available. The software tool paper aims to present software tool that was made per request of Faculty Hospital, however analysis of different pattern of radiographic findings would be quite an interesting suggestion for a full research article. 2) Please describe, if you had success implementing the image analysis in your clinical practice. What are the benefits and/or barriers of using the analysis in the real world? The ImageJ software tool was developed on request by the Facultypital of Královské Vinohrady, and it has been used by the Radiology center since September 2021. The score was mainly used for additional estimation to help doctors estimate the severity of COVID-19 disease in hospitalized patients. The first benefit is having a quick estimation of lung coverage by disease, which can be challenging to estimate by visual inspection. The second benefit is that the ImageJ and this specific SW Tool do not need installation (which can be challenging at the hospital IT infrastructure) and works on a average office computer. The challenge is mentioned inter and intra variability per user, but logging the whole process helps with that. In meantime we did optimization and some correction of reported issues of our ImageJ scripts, you are able to find them in the linked GitHub repository. It involves time and result robustness tests , inter and intra variance overview from reported parameters used, a new version including updated log protocols to help users troubleshoot possible issues with the version of ImageJ. New logs from both 8 and 16-bit versions of scripts now contain both the ImageJ version used and Bio-Image version used. We will welcome any issue reports or enhancement suggestions in the GitHub repository. Thank you again for your valuable feedback. Martin Schätz Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 03 Jul 2023 Martin Schätz , Department of Computing and Control Engineering, University of Chemistry and Technology, Prague, 166 28, Czech Republic 03 Jul 2023 Author Response Dear Dr. Tamas Dolinay, I want to express my sincere gratitude for taking the time to review our work and providing us with such thoughtful feedback. Your comments have helped ... Continue reading Dear Dr. Tamas Dolinay, I want to express my sincere gratitude for taking the time to review our work and providing us with such thoughtful feedback. Your comments have helped us to strengthen our work significantly, and I appreciate your efforts. Our replies to your major comments: 1) The abstract describes 5 different pattern of radiographic findings, but I do not find these in the manuscript. A novelty of the article would be the stratification of pneumonia based on the imaging. We appreciate your comments and would like to address your concern about the radiographic findings described in the abstract. The 5 groups are distinguished based on the percentage coverage in the lungs. We appologize for the confusion. We will revise the abstract to more accurately reflect your comment. The 5 different scores are originally based on paper „Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis“ (https://doi.org/10.1007/s00330-020-07033-y) where the lungs are divided and the result of each part is summed up to the score. It would be possible to do the same with 3D Slicer and the „Lung CT Analyzer“ (https://github.com/rbumm/SlicerLungCTAnalyzer) deep learning tool, however, it would require installing it and having more powerful hardware available. The software tool paper aims to present software tool that was made per request of Faculty Hospital, however analysis of different pattern of radiographic findings would be quite an interesting suggestion for a full research article. 2) Please describe, if you had success implementing the image analysis in your clinical practice. What are the benefits and/or barriers of using the analysis in the real world? The ImageJ software tool was developed on request by the Facultypital of Královské Vinohrady, and it has been used by the Radiology center since September 2021. The score was mainly used for additional estimation to help doctors estimate the severity of COVID-19 disease in hospitalized patients. The first benefit is having a quick estimation of lung coverage by disease, which can be challenging to estimate by visual inspection. The second benefit is that the ImageJ and this specific SW Tool do not need installation (which can be challenging at the hospital IT infrastructure) and works on a average office computer. The challenge is mentioned inter and intra variability per user, but logging the whole process helps with that. In meantime we did optimization and some correction of reported issues of our ImageJ scripts, you are able to find them in the linked GitHub repository. It involves time and result robustness tests , inter and intra variance overview from reported parameters used, a new version including updated log protocols to help users troubleshoot possible issues with the version of ImageJ. New logs from both 8 and 16-bit versions of scripts now contain both the ImageJ version used and Bio-Image version used. We will welcome any issue reports or enhancement suggestions in the GitHub repository. Thank you again for your valuable feedback. Martin Schätz Dear Dr. Tamas Dolinay, I want to express my sincere gratitude for taking the time to review our work and providing us with such thoughtful feedback. Your comments have helped us to strengthen our work significantly, and I appreciate your efforts. Our replies to your major comments: 1) The abstract describes 5 different pattern of radiographic findings, but I do not find these in the manuscript. A novelty of the article would be the stratification of pneumonia based on the imaging. We appreciate your comments and would like to address your concern about the radiographic findings described in the abstract. The 5 groups are distinguished based on the percentage coverage in the lungs. We appologize for the confusion. We will revise the abstract to more accurately reflect your comment. The 5 different scores are originally based on paper „Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis“ (https://doi.org/10.1007/s00330-020-07033-y) where the lungs are divided and the result of each part is summed up to the score. It would be possible to do the same with 3D Slicer and the „Lung CT Analyzer“ (https://github.com/rbumm/SlicerLungCTAnalyzer) deep learning tool, however, it would require installing it and having more powerful hardware available. The software tool paper aims to present software tool that was made per request of Faculty Hospital, however analysis of different pattern of radiographic findings would be quite an interesting suggestion for a full research article. 2) Please describe, if you had success implementing the image analysis in your clinical practice. What are the benefits and/or barriers of using the analysis in the real world? The ImageJ software tool was developed on request by the Facultypital of Královské Vinohrady, and it has been used by the Radiology center since September 2021. The score was mainly used for additional estimation to help doctors estimate the severity of COVID-19 disease in hospitalized patients. The first benefit is having a quick estimation of lung coverage by disease, which can be challenging to estimate by visual inspection. The second benefit is that the ImageJ and this specific SW Tool do not need installation (which can be challenging at the hospital IT infrastructure) and works on a average office computer. The challenge is mentioned inter and intra variability per user, but logging the whole process helps with that. In meantime we did optimization and some correction of reported issues of our ImageJ scripts, you are able to find them in the linked GitHub repository. It involves time and result robustness tests , inter and intra variance overview from reported parameters used, a new version including updated log protocols to help users troubleshoot possible issues with the version of ImageJ. New logs from both 8 and 16-bit versions of scripts now contain both the ImageJ version used and Bio-Image version used. We will welcome any issue reports or enhancement suggestions in the GitHub repository. Thank you again for your valuable feedback. Martin Schätz Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 17 Mar 2022 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 Version 3 (revision) 25 Jul 25 read Version 2 (revision) 03 Jul 23 read read Version 1 17 Mar 22 read read Tamas Dolinay , University of California, Los Angeles, Los Angeles, USA Hamid A. Jalab , Universiti Malaya, Kuala Lumpur, Malaysia Alessandro Santini , Humanitas University, Milan, Italy Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Santini A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 19 Aug 2025 | for Version 3 Alessandro Santini , Department of Biomedical Sciences, Humanitas University, Milan, Italy 0 Views copyright © 2025 Santini A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions I thank the Authors for their review and update of the manuscript, which I feel is now suitable for indexing. Competing Interests No competing interests were disclosed. Reviewer Expertise Acute respiratory distress syndrome, ventilator-induced lung injury, critical care medicine I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Santini A. Peer Review Report For: Estimation of Covid-19 lungs damage based on computer tomography images analysis [version 3; peer review: 2 approved, 1 not approved] . F1000Research 2025, 11 :326 ( https://doi.org/10.5256/f1000research.184052.r400406) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/11-326/v3#referee-response-400406 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2023 Santini A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 01 Nov 2023 | for Version 2 Alessandro Santini , Department of Biomedical Sciences, Humanitas University, Milan, Italy 0 Views copyright © 2023 Santini A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (2) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The Authors present a new tool for quantitative analysis of CT scans of COVID-19 patients. The novelty of this tool, compared to already available softwares such as 3D Slicer, is its availability (the code is open-source) and the alleged ease of use even by non-experienced users. However, when presenting results of inter- and intra-user variability of results, the Authors show not very promising results. They also acknowledge this problem in the discussion: "The biggest challenge in using this tool is an individual perception of images (...) Based on this a user can add the biggest bias even though the underlying data analysis is done correctly". While I agree with the Authors, I do not see how this problem is overcome by their tool. The Authors later in the manuscript state that "this challenge can be addressed by using standardized procedures and guidelines, multiple raters for segmentation, and computer-aided methods". However, it is not clear from this paper that this challenge has efficiently being addressed/solved. This is not a secondary issue for a tool which is designed to be "user-friendly" and easy to use even by a non-experienced user. I suggest the Authors to expand their inter- and intra-user variability analysis. While inter-user variability is presented, I did not find any data on intra-user variability (same user performing the analysis on a single CT more than once). You can use as a guide the following ref: Popović ZB and Thomas JD "Assessing observer variability: a user’s guide" Cardiovasc Diagn Ther. 2017 Jun; 7(3): 317–324. Furthermore, as the tool is supposed to give the same results as other, already available, softwares, the Authors should present a comparison between the results obtained with their tool and the results obtained with other(s) software(s) on their CT scans. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Yes Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Partly Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Partly References 1. Popović ZB, Thomas JD: Assessing observer variability: a user's guide. Cardiovasc Diagn Ther . 2017; 7 (3): 317-324 PubMed Abstract | Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Acute respiratory distress syndrome, ventilator-induced lung injury, critical care medicine I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (2) Author Response 30 Nov 2023 Martin Schätz, Department of Mathematics, Informatics and Cybernetics, University of Chemistry and Technology, Prague, 166 28, Czech Republic Dear Dr. Alessandro Santini, we express our sincere gratitude for your diligent review of our manuscript and for providing constructive and insightful comments. We wish to address your suggestion for the comparison of our tool with other software tools first. Numerous freeware programs exist for visualizing scientific image data, such as Imaris Viewer ( https://imaris.oxinst.com/imaris-viewer ), ZEISS ZEN lite ( https://www.zeiss.com/microscopy/en/products/software/zeiss-zen-lite.html ), and open-source software like 3D Slicer and FIJI/ImageJ, which offer manual BioImage Analysis and script creation capabilities. Analogous to CT software, these applications typically afford scientific data visualization, with open-source variants featuring additional tools or plugins for specific and systematic analyses. While we appreciate the suggestion to compare our tool with the Slicer Lung CT Analyzer extension of 3D Slicer, it is important to note that, as of the current date (21.11.2023), the extension is still in development and remains unpublished (you can find details on their GitHub repository: https://github.com/rbumm/SlicerLungCTAnalyzer ). We eagerly anticipate the extension's publication and look forward to a thorough comparison, as their design is sound, and offers a good alternative in case of access to the proper hardware. Publishing software tools takes time and when Slicer Lung CT Analyzer extension is published, it deserves a proper citation and acknowledgment. We value your feedback regarding the improvement of the use case and the suggestion to expand the paper more. To clarify our aim for user-friendliness, the tool was developed with the goal in mind, that „guessing“ volumetric percentage out of CT viewer is not an exact quantitative analysis. While manual analysis in other software is possible after user training, our tool's systematic approach, documented parameters, and step-by-step guide enable hospital staff to apply it with reasonable variability. During the tool's pilot implementation at FNKH Faculty Hospital, our focus was on lowering inter-user variability, as reflected in our report. However, what was tested was a score comparison between CT of the same patient with different slice thicknesses (CT1_1 with 0.6mm slices and CT1_2 with 3 mm slices). It was originally designed to assess score variability with different slice thicknesses. This comparison also provides insight into intra-user variability; however, we acknowledge that the number of repetitions may have some limitations. Once again, we sincerely appreciate your invaluable feedback. Best regards, Martin Schätz View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Author Response 09 Aug 2025 Martin Schätz, Department of Mathematics, Informatics and Cybernetics, University of Chemistry and Technology, Prague, 166 28, Czech Republic Dear reviewer, thank you very much for insightfull feedback and recomendations to improve our analysis of our tool. Addressing User Bias and Tool's Focus We agree that user perception introduces inherent variability in manual segmentation, a challenge common to all semi-automatic tools. Our tool addresses this not by eliminating user bias entirely, but by simplifying the segmentation process, making it more accessible and reducing errors from completely manual focus often used in hospitals. Its primary focus is on providing a standardized framework for quantitative analysis. This approach, coupled with full reproducibility through detailed logging of all sub-results, is particularly beneficial in a hospital setting where multiple raters often compare results. This was our key motivation for initially focusing on inter-user variability, as a single rater typically doesn't repeat the analysis on the same scan in clinical practice. Intra- and Inter-User Variability Analysis We appreciate the suggestion to expand our variability analysis. We've now included both intra-user and inter-user variability analyses in the updated manuscript. Jupyter notebooks with analysis, data and subresults are included in Zenodo and also available on GitHub, which is more user friendly for browsing such files. We would like to thank you for the helpful reference (Popović ZB and Thomas JD "Assessing observer variability: a user’s guide" Cardiovasc Diagn Ther. 2017 Jun; 7(3): 317–324), which guided our approach and is cited in the paper. Comparison with Existing Software Our tool is specifically developed for quantitative analysis in a hospital environment. This setting is often strict about software installation and may lack the necessary hardware to run complex neural network or deep learning models efficiently , if at all. While we acknowledge the importance of comparing our results with other published software, the specific 3D Slicer plugin relevant to our approach is still under development, as noted in its GitHub repository ( https://github.com/Slicer/SlicerLungCTAnalyzer ). We plan to conduct a thorough comparison with this and potentially other tools as they become finalized and published. At the moment the creators of this tool recomends/references general CT neural network segmenters which are useful for lung segmentation or segmentation of anatomic structures from CT. Unfortunately, that is only substep in whole analysis workflow. We agree the comparison is a crucial point for future versions and further analysis of our tool. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Santini A. Peer Review Report For: Estimation of Covid-19 lungs damage based on computer tomography images analysis [version 3; peer review: 2 approved, 1 not approved] . F1000Research 2025, 11 :326 ( https://doi.org/10.5256/f1000research.147768.r211856) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/11-326/v2#referee-response-211856 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2023 Dolinay T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 18 Jul 2023 | for Version 2 Tamas Dolinay , Department of Medicine, University of California, Los Angeles, Los Angeles, CA, USA 0 Views copyright © 2023 Dolinay T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The authors have sufficiently revised the manuscript. I believe the revised paper is an important addition to the growing field of CT imaging in COVID-19 lung disease. Competing Interests No competing interests were disclosed. Reviewer Expertise Pulmonology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Dolinay T. Peer Review Report For: Estimation of Covid-19 lungs damage based on computer tomography images analysis [version 3; peer review: 2 approved, 1 not approved] . F1000Research 2025, 11 :326 ( https://doi.org/10.5256/f1000research.147768.r183705) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/11-326/v2#referee-response-183705 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2022 Jalab H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 25 Oct 2022 | for Version 1 Hamid A. Jalab , [email protected] Hamid A. Jalab Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia 0 Views copyright © 2022 Jalab H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The study describes a novel image tool developed for CT assessment of COVID-19 patients. The tool was created to assist in estimating the percentage of lungs infected by the virus. The manuscript is interesting and addresses an important issue. Major comments: 1. The Introduction should provide a strong argument for why the software tool is important. 2. It is of importance to have sufficient results to justify the novelty of the proposed software tool. 3. The robustness of the proposed software tool has not been addressed; this should be emphasized in the discussion section. 4. What are the benefits of the proposed approach above other current the new software tool? Is the rationale for developing the new software tool clearly explained? No Is the description of the software tool technically sound? No Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? No Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? No Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? No Competing Interests No competing interests were disclosed. Reviewer Expertise image processing I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (1) Author Response 03 Jul 2023 Martin Schätz, Department of Computing and Control Engineering, University of Chemistry and Technology, Prague, 166 28, Czech Republic Dear Dr. Hamid A. Jalab, I am extremely grateful for the time and effort you have dedicated to reviewing our Software Tool Article. Your constructive comments have been instrumental in shaping our manuscript, and I truly appreciate your contributions to the peer review process. Our replies to your comments: 1. The Introduction should provide a strong argument for why the software tool is important. Thank you for your valuable feedback. We agree that the Introduction should provide a strong argument for the software tool's importance. As we highlighted in the new paragraph, there is a need for a open source, user-friendly software tool for reproducible quantitative analysis of CT scans to estimate COVID lung pneumonia. Current software tools such as ImageJ and 3D Slicer may not be user-friendly for end-users who are not familiar with creating analysis workflows. Our software tool aims to fill this gap by providing a user-friendly solution for reproducible quantitative analysis, with available code, training data, tutorial and GitHub repository. 2. It is of importance to have sufficient results to justify the novelty of the proposed software tool. The main motivation for the software tool was that no other similar software tool was available for use on the computer infrastructure of Faculty Hospital of Královské Vinohrady. The main obstacles were in internal network policy, the unavailability of any high performance computing hardware, and need of specific tool. Since more hospitals might be challenged in similar ways, the highest motivation was to share our work openly and freely to help innovate and enable them. 3. The robustness of the proposed software tool has not been addressed; this should be emphasized in the discussion section. Since the software tool is user operated, there is high inter and intra variability of results – which we addressed in text. The robustness of whole workflow is now addressed in terms of repeatability. 4. What are the benefits of the proposed approach above other current the new software tool? The only other open-source software tool adressing similar medical analysis is plugin SlicerLungCTAnalyzer for 3D Slicer software (available on GitHub: https://github.com/rbumm/SlicerLungCTAnalyzer). Of course there also exists commercially available software like Thoracic VCAR and others, but the money and hardware requirements might render these software unreachable for a lot of facilities, and since the tools are closed it might be enough for diagnosis but not for other research. The benefits of our Software tool are that it is developed open-source, in its very low hardware requirements and its ease of use with low technical requirements for installation. These were also the requirements of the faculty hospital where it was requested. Is the rationale for developing the new software tool clearly explained? We added a comparison to other freely available open source software tool as requested. Is the description of the software tool technically sound? We improved the pseudo-code on top of the available source code, which should improve the description overview of the whole tool. Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? The whole original code and its update is available through a linked (now updated) repository and on GitHub with a small tutorial. The data and software availability parts were extended. We added pseudocode for a better overview, but best option is to go through the code. Any encountered problems with the macro on specific machines and/or ImageJ version can be reported through an issue on GitHub repository. Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? The output of the software tool is a log of whole process and partial results, it is a necessary step for reproducibility, good data management and interpretation. If you encountered any issues with the tool or prepared protocols please report them in GitHub repositories so we can properly address them. We are happy to help, sustainability is important part of our project. Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? We included more analysis about performance of the tool and also inter and intra variability overview (also available on GitHub). Scripts are included in linked materials with both new version of Software Tool. In the meantime we did optimization and some correction of reported issues of our ImageJ scripts, you are able to find them in the linked GitHub repository. It involves time and result-robustness tests , inter and intra variance overview from reported parameters used, a new version including updated log protocols to help users troubleshoot possible issues with the version of ImageJ. New logs from both 8 and 16-bit versions of scripts now contain both the ImageJ version used and Bio-Image version used. We will welcome any issue reports or enhancement suggestions in the GitHub repository. Thank you again for your valuable feedback. Martin Schätz View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Jalab HA. Peer Review Report For: Estimation of Covid-19 lungs damage based on computer tomography images analysis [version 3; peer review: 2 approved, 1 not approved] . F1000Research 2025, 11 :326 ( https://doi.org/10.5256/f1000research.120473.r153263) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/11-326/v1#referee-response-153263 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2022 Dolinay T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 10 Aug 2022 | for Version 1 Tamas Dolinay , Department of Medicine, University of California, Los Angeles, Los Angeles, CA, USA 0 Views copyright © 2022 Dolinay T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Dr. Schätz and colleagues describe a new image analysis modality to quickly review and score lung CT scan images. The image analysis was applied to patients with COVID-19 pneumonia to aid with diagnostic accuracy. It is increasingly recognized that viral pneumonias, including COVID-19-associated pneumonia, may have a specific radiographic pattern. Automated modalities, including the one described by the authors can help with the rapid identification viral pneumonias and with measurement of the extent of the disease. The manuscript is interesting and covers an important topic, but its current format suffers from a few weaknesses. Major comments: The abstract describes 5 different pattern of radiographic findings, but I do not find these in the manuscript. A novelty of the article would be the stratification of pneumonias based on the imaging. Please describe, if you had success implementing the image analysis in your clinical practice. What are the benefits and/or barriers of using the analysis in the real world? Minor comments: Please write out the abbreviation when used for the first time to help the reader, who is not necessarily familiar with the specific language used. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Yes Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Pulmonology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 03 Jul 2023 Martin Schätz, Department of Computing and Control Engineering, University of Chemistry and Technology, Prague, 166 28, Czech Republic Dear Dr. Tamas Dolinay, I want to express my sincere gratitude for taking the time to review our work and providing us with such thoughtful feedback. Your comments have helped us to strengthen our work significantly, and I appreciate your efforts. Our replies to your major comments: 1) The abstract describes 5 different pattern of radiographic findings, but I do not find these in the manuscript. A novelty of the article would be the stratification of pneumonia based on the imaging. We appreciate your comments and would like to address your concern about the radiographic findings described in the abstract. The 5 groups are distinguished based on the percentage coverage in the lungs. We appologize for the confusion. We will revise the abstract to more accurately reflect your comment. The 5 different scores are originally based on paper „Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis“ (https://doi.org/10.1007/s00330-020-07033-y) where the lungs are divided and the result of each part is summed up to the score. It would be possible to do the same with 3D Slicer and the „Lung CT Analyzer“ (https://github.com/rbumm/SlicerLungCTAnalyzer) deep learning tool, however, it would require installing it and having more powerful hardware available. The software tool paper aims to present software tool that was made per request of Faculty Hospital, however analysis of different pattern of radiographic findings would be quite an interesting suggestion for a full research article. 2) Please describe, if you had success implementing the image analysis in your clinical practice. What are the benefits and/or barriers of using the analysis in the real world? The ImageJ software tool was developed on request by the Facultypital of Královské Vinohrady, and it has been used by the Radiology center since September 2021. The score was mainly used for additional estimation to help doctors estimate the severity of COVID-19 disease in hospitalized patients. The first benefit is having a quick estimation of lung coverage by disease, which can be challenging to estimate by visual inspection. The second benefit is that the ImageJ and this specific SW Tool do not need installation (which can be challenging at the hospital IT infrastructure) and works on a average office computer. The challenge is mentioned inter and intra variability per user, but logging the whole process helps with that. In meantime we did optimization and some correction of reported issues of our ImageJ scripts, you are able to find them in the linked GitHub repository. It involves time and result robustness tests , inter and intra variance overview from reported parameters used, a new version including updated log protocols to help users troubleshoot possible issues with the version of ImageJ. New logs from both 8 and 16-bit versions of scripts now contain both the ImageJ version used and Bio-Image version used. We will welcome any issue reports or enhancement suggestions in the GitHub repository. Thank you again for your valuable feedback. Martin Schätz View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Dolinay T. Peer Review Report For: Estimation of Covid-19 lungs damage based on computer tomography images analysis [version 3; peer review: 2 approved, 1 not approved] . F1000Research 2025, 11 :326 ( https://doi.org/10.5256/f1000research.120473.r146294) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/11-326/v1#referee-response-146294 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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