Methodology for Pediatric Head Computed Tomography Image Segmentation and Volumetric Calculation Using a Tablet Computer and Stylus Pen

Methodology for Pediatric Head Computed Tomography Image Segmentation and Volumetric Calculation Using a Tablet Computer and Stylus Pen | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Methodology for Pediatric Head Computed Tomography Image Segmentation and Volumetric Calculation Using a Tablet Computer and Stylus Pen Hiroaki Hashimoto, Makoto Shimada, Osamu Takemoto, Yasuyoshi Chiba This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5411523/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Dec, 2024 Read the published version in Child's Nervous System → Version 1 posted 9 You are reading this latest preprint version Abstract Purpose This study presents a MATrix LABoratory (MATLAB)-based methodology for calculating intracranial volumes from head computed tomography (CT) data and compares it with established methods. Methods Regions of interest (ROI) were manually segmented on CT images using a stylus pen, facilitated by mirroring a computer desktop onto a tablet. The volumetric process involved three main steps: (1) calculating the volume of a single voxel, (2) counting the total number of voxels within the segmented ROI, and (3) multiplying this voxel count by the single-voxel volume. This method was applied to 83 pediatric head CT scans from patients with minor head trauma, and the volumetric results were compared with those obtained from OsiriX. Results A paired t-test revealed a statistically significant difference (p < 0.001) between the volumes obtained with our MATLAB-based method and those from OsiriX. However, the volumes obtained by our method were only 0.32% higher than the OsiriX measurements. Conclusion Although a statistically significant difference was found between our method and OsiriX, the discrepancy is minimal and practically negligible. The MATLAB code provided in this study may serve as a valuable tool for beginners in radiological research. Image segmentation volumetric measurement CT stylus pen tablet computer Figures Figure 1 Figure 2 Figure 3 Introduction Volumetric measurements derived from computed tomography (CT) imaging [1] and magnetic resonance imaging (MRI) [2-4] have been widely utilized for quantitative research, including the development of normal growth curves for healthy children [2, 5, 6]. Additionally, volumetric assessments have been used to quantitatively evaluate patients with various conditions, such as Chiari malformation type I [3, 7] or type II [8]. In the 1990s, intracranial volumetric measurements were roughly estimated by multiplying the transverse and anteroposterior diameters with the slice thickness obtained from CT images [1]. Subsequently, a system based on the C programming language was introduced, enabling segmentation of regions of interest (ROI) [9, 10]. The Cavalieri method has been applied to calculate the volumes of intracranial structures [11, 12], along with semi-automated segmentation programs such as the open-source software ITK-SNAP (the University of Pennsylvania, Philadelphia, PA, USA) [4]. Commercial software such as Amira (Thermo Fisher Scientific Inc., Waltham, MA, USA) [13], the Philips iSite enterprise System (Philips, Amsterdam, the Netherlands) [8], OsiriX (Pixmeo, Bernex, Switzerland) [14], and iPlan (Brainlab, Munich, Germany) [15, 16] have been employed for obtaining volumetric data. A custom tool developed using MATrix LABoratory (MATLAB, MathWorks, Natick, MA, USA) for calculating PCFV or ventricular volume has also been reported [2, 7]. The continuous advancement in volumetric measurement techniques has expanded the range of available tools, offering significant benefits but also presenting challenges for beginners in selecting the most suitable method. This study presents a MATLAB-based approach for segmentation and volumetric calculation of Digital Imaging and Communications in Medicine (DICOM) CT data using a tablet computer and stylus pen. Our results were compared with those obtained from other established tools, such as OsiriX. The MATLAB code is provided (see Supplemental Data), and we believe that this ready-to-use tool will be particularly advantageous for beginners seeking an accessible solution for volumetric analysis. Methods Patients and Study Setting To evaluate our methodology, we calculated intracranial volume from head CT scans. We retrospectively enrolled DICOM-CT images from pediatric patients who underwent CT examinations for minor head trauma between March 2006 and May 2023 at Osaka Women’s and Children’s Hospital. To minimize the influence of disease or trauma, we applied the following exclusion criteria: (1) suspicion of abuse, (2) need for craniotomy for decompression within a few days after a head injury, (3) cranial depressed fracture requiring surgery, (4) apparent compression or deformation of lateral ventricles due to hematoma, (5) Ommaya reservoir implantation or drainage surgeries, (6) presence of complications such as craniosynostosis, tumor, epilepsy, autism, intracranial arachnoid cyst, chromosomal abnormalities, cardiovascular disease, and endocrine disorder etc., and (7) presence of cavum Vergae or cavum septum. Therefore, all subjects were healthy before the head trauma and did not require any surgical intervention. Additionally, since pediatric intracranial volume increases rapidly and can grow to two to three times its size by the age of two years [6], we limited the inclusion criteria to subjects between two and ten years of age to minimize variability due to growth effects. The study population was derived from our previous research [5, 6]. CT Protocols All CT scans were acquired with a 0.5 mm slice width, and images reconstructed with a 5 mm slice thickness were used for volume calculations. CT scans were performed using either Aquilion ONE or Aquilion Prime SP (Canon Medical Systems, Otawara, Japan). Due to the retrospective nature of this study, achieving consistency in CT protocols was not possible. The details of the CT protocols are presented in Supplemental Table 1. Volume Calculation Using Our Methodology Our method for calculating the volume based on DICOM images is as follows: Firstly, we calculate the area of one pixel by multiplying its length and width, as recorded in the DICOM data. Next, we calculate the volume of one voxel by multiplying this area by the slice thickness (a pixel in the three-dimensional space is called a voxel). Finally, we count the total number of pixels within the segmented ROI and calculate the volume of the segmented ROI by multiplying this total by the volume of one voxel. A similar methodology has been described in a previous study [10]. Detailed our process for volume calculations is illustrated in Figure 1. The DICOM data were initially exported as compact disc-recordable (CD-R) in our hospital for security reasons (Fig.1a). The DICOM data on the CD-R were loaded and exported as a single file with the .dcm extension using Mango software (Multi-image Analysis graphical user interface, v4.1, available at https://mangoviewer.com/index.html) (Fig.1b). The DICOM data in the .dcm format were then imported into MATLAB R2023a (Fig.1c) using the Image Processing Toolbox. We utilized the Image Segmenter app in MATLAB (https://www.mathworks.com/help/images/ref/imagesegmenter-app.html). To manually segment ROI from the original DICOM images, we used the imageSegmenter function implemented in the Image Processing Toolbox (https://mathworks.com/help/images/image-segmentation-using-the-image-segmenter-app.html). To facilitate this segmentation process and improve efficiency, we mirrored the desktop displaying the Image Segmenter onto a tablet computer (in our case, a 12.9-inch iPad Pro 5th generation, Apple, Inc., Cupertino, CA). To obtain segmentation, we manually traced the ROI on the tablet computer screen using a stylus pen (in our case, Apple Pencil 2nd generation, Apple, Inc., Cupertino, CA) (Fig.1d). Representative intracranial segmentations from a single subject, along with the original CT images, are presented in Figure 2. Once all ROI segments were obtained (Fig.1e), we counted the total number of pixels in the segments and obtained volumetric values (in milliliter, mL) (Fig.1f). The segmentation process was performed by a single examiner (Hiroaki H). MATLAB code related to this procedure is provided in the Supplemental Data. Volume Calculation Using OsiriX The .dcm files generated by the method described above were imported into OsiriX. The OsiriX display was mirrored onto a tablet computer, where the ROI was manually traced using a stylus pen to obtain segmentation, following the same methodology described above. A single examiner (Hiroaki H) also performed the segmentation process. After ROI segmentation, OsiriX calculates the ROI volume (mL). Statistical Analyses We generated two sets of volumetric data using our MATLAB-based method and Osirix, both derived from the same subject population and CT scans. For each subject, two different volumetric values were calculated, one from the MATLAB method and the other from OsiriX. Since these two data sets correspond to the same subjects, a paired t-test was performed to assess the differences between the two methods. This test evaluates whether the volumetric measurements obtained from the MATLAB method significantly differ from those obtained with OsiriX. In addition, an unpaired t-test was conducted to explore potential differences in volumetric data sets, treating them as if they were independent samples from the same population, obtained using two distinct methodologies. This approach tested whether the MATLAB method and OsiriX produced significantly different volumetric data sets. Statistical significance was defined as p-values <0.05. Additionally, a power analysis was conducted to determine the effect size (Cohen’s d) and power (1 - beta). Results Baseline Characteristics A total of 83 CT scans from pediatric patients with minor head trauma were analyzed according to the exclusion and inclusion criteria, with 33 females (39.8%). The mean age at the time of the CT scans was 66.55 ± 30.72 months. The volumetric values calculated using our MATLAB method had a mean of 1292.68 ± 128.02mL. The volumetric values calculated using OsiriX had a mean of 1288.60 ± 128.03mL. Results from Paired t-test We compared the volumetric values calculated using our MATLAB method with those from OsiriX using a paired t-test. A significant difference was observed (p < 0.001, Fig. 3a), indicating that the volumetric values obtained using our MATLAB method were statistically different from those obtained using OsiriX. The difference between values obtained from our MATLAB method and those from OsiriX for each patient had a mean of 4.09 ± 9.12 mL. The distribution of these differences is shown in Figure 3b. On average, the volumetric values from our MATLAB method were 4.09 mL higher than those from OsiriX. This corresponds to 0.32 % of the intracranial volume. Results from Unpaired t-test The distributions of volumetric values from our MATLAB method and OsiriX are presented in Figure 3c. An unpaired t-test revealed no statistically significant difference between the two groups (p = 0.84). This suggests that, when treated as independent samples, the volumetric groups produced by our MATLAB method and Osirix did not differ significantly. Power Analysis The results of the power analysis for the paired and unpaired t-tests used in this study are shown in Table 1. The power of the paired t-test was high (0.98), confirming that the volumetric values obtained using our MATLAB method were statistically different from those obtained using OsiriX. However, the power of the unpaired t-test was low (0.06), due to the small effect size (0.032). The sample size required for a power of 0.8 was estimated to be 7709. The beta error was calculated as 0.94 (1 – 0.06), indicating that there is a high risk of incorrectly accepting the null hypothesis in the unpaired t-test. Table 1. Results of power analysis. Test Type Effect size (Cohen’s d) Sample size for 0.8 power Power (1 - beta) Paired t-test 0.44 42 0.98 Unpaired t-test 0.032 7709 0.060 For both paired and unpaired t-test conditions, the effect size was calculated based on Cohen’s d, the required sample size to achieve a power of 0.8 was determined, and the power values were calculated. Discussion This study introduces a methodology for calculating intracranial volume from head CT DICOM data using custom MATLAB code, a tablet computer, and a stylus pen. To evaluate the accuracy of our MATLAB-based method, we compared it with an established method, OsiriX. The paired t-test revealed a significant difference between the two methods, with the volumetric values obtained using our MATLAB method being 0.32 % larger than those from OsiriX. OsiriX is a well-established DICOM viewer [17], recognized for its user-friendly interface, ability to load detailed images efficiently, and the fact that it is both open-source and free of charge [18]. OsiriX has been widely used for volumetric assessments [19-21], which is why we selected it to evaluate the accuracy of our developed MATLAB-based method. Although we anticipated no significant difference between the results of our MATLAB method and OsiriX, the paired t-test revealed a statistically significant difference. One potential source of this discrepancy is the manual segmentation of ROIs, which may have introduced slight variations between the areas segmented using our method and OsiriX. Since all segmentations in this study were performed by a single examiner (H. H.), the variation due to manual segmentation was consistent across all samples, and we believe that the effects of this variability were minimized by analyzing a larger sample size. Therefore, we infer that the observed statistical difference between the two methods is likely due to methodological differences rather than segmentation-related variation. While we cannot definitively explain the cause of this difference, we noticed that manually segmented ROIs tended to appear slightly smaller in OsiriX compared to our MATLAB method. This suggests that the measurements obtained using Osirix were, on average, 4.09 mL smaller than those obtained using our method. However, this remains an assumption. Given the average intracranial volume of 1292.68 mL obtained by our method, the difference represents a small proportion (0.32 %), leading us to conclude that this difference may be negligible for practical purposes. In our previous research, we used this MATLAB-based method to calculate volumes of various structures. For example, using head MRI, we quantified lesion volumes on diffusion-weighted imaging, demonstrating differential outcomes between groups undergoing mechanical thrombectomy [22]. Similarly, using head CT, we measured chronic subdural hematoma volumes, identifying differences between recurrence and non-recurrence groups [23]. Additionally, we have published several studies investigating intracranial structural volumes in the Japanese pediatric population [5, 6, 24]. In this study, while we focused on intracranial volume using head CT images, our method can be applied to MRI DICOM data as well. Moreover, manual segmentation enables the measurement of regions beyond the intracranial volume. We suggest that readers consider using this method to segment various structures with CT or MRI DICOM data. Manual segmentation, as employed in our MATLAB method, requires additional time and effort. To mitigate this issue, we proposed using a stylus pen for tracing segmented lines on a tablet computer screen, which mirrors the desktop display. This approach has been reported previously [25], showing that stylus pen-based volumetric techniques offer a technological advancement over traditional mouse-based tracing by providing improved statistical accuracy and significantly reducing the time required [26]. Therefore, we recommend using our MATLAB method in combination with a tablet computer and a stylus pen to enhance efficiency and accuracy. This study used a 5 mm slice width for volume calculation. However, CT images with a 1mm slice width are now widely used at our hospital, allowing for more accurate image evaluation. At our institution, head CT images are initially acquired with a 0.5 mm slice width and then reconstructed into 5 mm or 1 mm slices. While 5 mm slices are routinely reconstructed in all cases, 1mm slices are only reconstructed when necessary to avoid unnecessary procedures. Additionally, as 1 mm slice images contain five times more data than 5 mm slices, the labor and time required for manual segmentation would be significantly increased. Since the primary goal of this study was to compare our MATLAB method with OsiriX, we opted to use 5mm slice CT images to minimize the time and effort needed for segmentation. This study has several limitations. Due to its retrospective nature, different CT protocols were used, and we were unable to ensure consistency across these protocols. Additionally, although we used 5 mm slice CT images, more accurate volumetric evaluations could be achieved with 1 mm slice images, which are preferable for precision. Conclusions The MATLAB-based methodology for calculating volume using DICOM images is presented, utilizing a tablet computer and stylus pen. While the volumetric values obtained using our method statistically differed from those calculated with Osirix, the difference was only 0.32%, which we consider negligible for practical purposes. The actual MATLAB code is provided in the Supplemental Data, allowing readers to implement our method easily. We hope that this study contributes to advancing your research efforts. Abbreviations CD-R , compact disc-recordable; CT , computed tomography; DICOM , Digital Imaging and Communications in Medicine; MATLAB , MATrix LABoratory; mL , milliliter; MRI, magnetic resonance imaging; ROI , regions of interest. Declarations Author contribution H.H. conceived the study, collected and analyzed the data, developed the MATLAB program, created all figures and tables, and was primarily responsible for writing the manuscript. M.S. collected the data. All authors provided clinical care and evaluated the patients. O.T. and Y.C. supervised the study. All authors have reviewed the manuscript. Funding The Japan Society for the Promotion of Science (JSPS) KAKENHI [JP21K16629 (Hiroaki Hashimoto)] supported this work. Data availability The data used in this study are available from the corresponding authors upon reasonable request and after additional ethics approval. Ethical approval and consent to participate Ethical approval was obtained from the Ethics Committee of Osaka Women’s and Children’s Hospital (Izumi, Japan, approval no. 1634), adhering to the Declaration of Helsinki. Informed consent was obtained through the opt-out method on our center’s website, considering the retrospective and noninvasive nature of the study. Consent for publication All authors reviewed the complete manuscript and consented to publication. Competing interest All authors have no competing interests or conflicts of interest to disclose. References Prassopoulos P, Cavouras D, Golfinopoulos S (1996) Developmental changes in the posterior cranial fossa of children studied by CT. Neuroradiology 38: 80-83 Cutler NS, Srinivasan S, Aaron BL, Anand SK, Kang MS, Altshuler DBet alKhalsa SSS (2020) Normal cerebral ventricular volume growth in childhood. 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J Med Syst 45: 1-12 Salonen HM, Åhlberg TM, Laitinen‐Vapaavuori OM, Mölsä SH (2022) CT measurement of prostate volume using OsiriX® viewer is reliable, repeatable, and not dependent on observer, CT protocol, or contrast enhancement in dogs. Vet Radiol Ultrasound 63: 729-738 Chuang C-C, Lin S-Y, Pai P-C, Yan J-L, Toh C-H, Lee S-Tet alLee C-C (2017) Different Volumetric Measurement Methods for Pituitary Adenomas and Their Crucial Clinical Significance. Sci Rep 7: 40792 Hashimoto H, Maruo T, Kimoto Y, Nakamura M, Fujinaga T, Nakamura H, Ushio Y (2023) The association between diffusion-weighted imaging-Alberta Stroke Program Early Computed Tomography Score and the outcome following mechanical thrombectomy of anterior circulation occlusion. Interdiscip Neurosurg 33: 101758 Hashimoto H, Maruo T, Kimoto Y, Nakamura M, Fujinaga T, Ushio Y (2023) Burr hole locations are associated with recurrence in single burr hole drainage surgery for chronic subdural hematoma. World Neurosurg: X 19: 100204 Hashimoto H, Irizato N, Takemoto O, Chiba Y (2024) Intracranial volumetric evaluation in postnatally repaired myelomeningocele infants. Childs Nerv Syst 40: 2851-2858 Salaffi F, Carotti M, Ciapetti A, Ariani A, Gasparini S, Grassi W (2013) Validity of a computer-assisted manual segmentation software to quantify wrist erosion volume using computed tomography scans in rheumatoid arthritis. BMC Musculoskelet Disord 14: 265 Perandini S, Faccioli N, Inama M, Pozzi Mucelli R (2011) Freehand liver volumetry by using an electromagnetic pen tablet: accuracy, precision, and rapidity. J Digit Imaging 24: 360-365 Additional Declarations No competing interests reported. Supplementary Files SupplementarydataNMCTechnicalNote.docx Cite Share Download PDF Status: Published Journal Publication published 23 Dec, 2024 Read the published version in Child's Nervous System → Version 1 posted Editorial decision: Revision requested 03 Dec, 2024 Reviews received at journal 02 Dec, 2024 Reviews received at journal 29 Nov, 2024 Reviewers agreed at journal 28 Nov, 2024 Reviewers agreed at journal 25 Nov, 2024 Reviewers invited by journal 23 Nov, 2024 Editor assigned by journal 08 Nov, 2024 Submission checks completed at journal 08 Nov, 2024 First submitted to journal 07 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5411523","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":382401726,"identity":"16164f26-a0b9-4d26-9703-6a1a958dc16d","order_by":0,"name":"Hiroaki Hashimoto","email":"data:image/png;base64,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","orcid":"","institution":"Osaka Women’s and Children’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hiroaki","middleName":"","lastName":"Hashimoto","suffix":""},{"id":382401729,"identity":"cda6a250-bedf-490b-9b39-50c7d4eeaeff","order_by":1,"name":"Makoto Shimada","email":"","orcid":"","institution":"Osaka Women’s and Children’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Makoto","middleName":"","lastName":"Shimada","suffix":""},{"id":382401730,"identity":"7c174718-d503-4476-9a19-6ce75b9d1549","order_by":2,"name":"Osamu Takemoto","email":"","orcid":"","institution":"Osaka Women’s and Children’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Osamu","middleName":"","lastName":"Takemoto","suffix":""},{"id":382401732,"identity":"be278bb9-75b5-406a-b3f1-d58088cb7efc","order_by":3,"name":"Yasuyoshi Chiba","email":"","orcid":"","institution":"Osaka Women’s and Children’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yasuyoshi","middleName":"","lastName":"Chiba","suffix":""}],"badges":[],"createdAt":"2024-11-07 16:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5411523/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5411523/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00381-024-06723-y","type":"published","date":"2024-12-23T15:57:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71539377,"identity":"b7974bc6-aa0a-4d3c-8fb2-860815708c55","added_by":"auto","created_at":"2024-12-16 14:25:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1974927,"visible":true,"origin":"","legend":"\u003cp\u003eSchema for volume calculation.\u003c/p\u003e\n\u003cp\u003eThe DICOM data are initially exported via CD-R (a) and then converted into a .dcm file using Mango software (b), which can be imported into MATLAB (c). The Image Segmenter app is used to obtain ROI, and the desktop is mirrored to a tablet computer. The ROI is manually traced on the screen using a stylus pen (d). Once all ROI segments are obtained (e), their volume is calculated (f).\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5411523/v1/3b4f8584e0850211b8841cf6.png"},{"id":71540011,"identity":"3a6e7ce0-0a57-41a8-babe-61b89ede24f8","added_by":"auto","created_at":"2024-12-16 14:33:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1525120,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative segmentation.\u003c/p\u003e\n\u003cp\u003eHead CT images acquired from a four-year-old male are presented (a), and segmentations focusing on the intracranial region are shown (b).\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5411523/v1/96e95b04fb9006edbee08043.png"},{"id":71540010,"identity":"80c6870a-29f8-4a23-88f0-cf14e212047c","added_by":"auto","created_at":"2024-12-16 14:33:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1576065,"visible":true,"origin":"","legend":"\u003cp\u003eResults of statistical analyses.\u003c/p\u003e\n\u003cp\u003e(a) Volumetric values calculated with our MATLAB method and OsiriX for each patient are connected by solid lines. A paired t-test reveals a statistically significant difference between the MATLAB method and OsiriX (p \u0026lt; 0.001). (b) Differences between methods were calculated by subtracting OsiriX values from MATLAB values for each patient (MATLAB - OsiriX), with the distribution of these differences shown. (c) Histograms depict the distributions of volumetric values obtained with our MATLAB method and OsiriX. An unpaired t-test, assuming the groups are independent, shows no significant (n.s.) difference between the two groups.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5411523/v1/ad3977ca278e5355571f8cfc.png"},{"id":72640557,"identity":"49e38008-3e4a-43e6-ad30-56d7b2ad11e8","added_by":"auto","created_at":"2024-12-30 16:06:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6265613,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5411523/v1/d6850a59-ea83-4dcb-abca-7c603adcc084.pdf"},{"id":71539375,"identity":"f5a70ee0-95da-4015-8a50-f4d7452ae039","added_by":"auto","created_at":"2024-12-16 14:25:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27848,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarydataNMCTechnicalNote.docx","url":"https://assets-eu.researchsquare.com/files/rs-5411523/v1/add551963f772d25e5af0e0a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Methodology for Pediatric Head Computed Tomography Image Segmentation and Volumetric Calculation Using a Tablet Computer and Stylus Pen","fulltext":[{"header":"Introduction","content":"\u003cp\u003eVolumetric measurements derived from computed tomography (CT) imaging [1] and magnetic resonance imaging (MRI) [2-4] have been widely utilized for quantitative research, including the development of normal growth curves for healthy children [2, 5, 6]. Additionally, volumetric assessments have been used to quantitatively evaluate patients with various conditions, such as Chiari malformation type I [3, 7] or type II [8].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the 1990s, intracranial volumetric measurements were roughly estimated by multiplying the transverse and anteroposterior diameters with the slice thickness obtained from CT images [1]. Subsequently, a system based on the C programming language was introduced, enabling segmentation of regions of interest (ROI) [9, 10]. The Cavalieri method has been applied to calculate the volumes of intracranial structures [11, 12], along with semi-automated segmentation programs such as the open-source software ITK-SNAP (the University of Pennsylvania, Philadelphia, PA, USA) [4]. Commercial software such as Amira (Thermo Fisher Scientific Inc., Waltham, MA, USA) [13], the Philips iSite enterprise System (Philips, Amsterdam, the Netherlands) [8], OsiriX (Pixmeo, Bernex, Switzerland) [14], and iPlan (Brainlab, Munich, Germany) [15, 16] have been employed for obtaining volumetric data. A custom tool developed using MATrix LABoratory (MATLAB, MathWorks, Natick, MA, USA) for calculating PCFV or ventricular volume has also been reported [2, 7].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe continuous advancement in volumetric measurement techniques has expanded the range of available tools, offering significant benefits but also presenting challenges for beginners in selecting the most suitable method. This study presents a MATLAB-based approach for segmentation and volumetric calculation of Digital Imaging and Communications in Medicine (DICOM) CT data using a tablet computer and stylus pen. Our results were compared with those obtained from other established tools, such as OsiriX. The MATLAB code is provided (see Supplemental Data), and we believe that this ready-to-use tool will be particularly advantageous for beginners seeking an accessible solution for volumetric analysis.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003ePatients and Study Setting\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate our methodology, we calculated intracranial volume from head CT scans. We retrospectively enrolled DICOM-CT images from pediatric patients who underwent CT examinations for minor head trauma between March 2006 and May 2023 at Osaka Women\u0026rsquo;s and Children\u0026rsquo;s Hospital.\u003c/p\u003e\n\u003cp\u003eTo minimize the influence of disease or trauma, we applied the following exclusion criteria: (1) suspicion of abuse, (2) need for craniotomy for decompression within a few days after a head injury, (3) cranial depressed fracture requiring surgery, (4) apparent compression or deformation of lateral ventricles due to hematoma, (5) Ommaya reservoir implantation or drainage surgeries, (6) presence of complications such as craniosynostosis, tumor, epilepsy, autism, intracranial arachnoid cyst, chromosomal abnormalities, cardiovascular disease, and endocrine disorder etc., and (7) presence of cavum Vergae or cavum septum. Therefore, all subjects were healthy before the head trauma and did not require any surgical intervention. Additionally, since pediatric intracranial volume increases rapidly and can grow to two to three times its size by the age of two years [6], we limited the inclusion criteria to subjects between two and ten years of age to minimize variability due to growth effects. The study population was derived from our previous research [5, 6].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCT Protocols\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll CT scans were acquired with a 0.5 mm slice width, and images reconstructed with a 5 mm slice thickness were used for volume calculations. CT scans were performed using either Aquilion ONE or Aquilion Prime SP (Canon Medical Systems, Otawara, Japan). Due to the retrospective nature of this study, achieving consistency in CT protocols was not possible. The details of the CT protocols are presented in Supplemental Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVolume Calculation Using Our Methodology\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOur method for calculating the volume based on DICOM images is as follows: Firstly, we calculate the area of one pixel by multiplying its length and width, as recorded in the DICOM data. Next, we calculate the volume of one voxel by multiplying this area by the slice thickness (a pixel in the three-dimensional space is called a voxel). Finally, we count the total number of pixels within the segmented ROI and calculate the volume of the segmented ROI by multiplying this total by the volume of one voxel. A similar methodology has been described in a previous study\u0026nbsp;[10].\u003c/p\u003e\n\u003cp\u003eDetailed our process for volume calculations is illustrated in Figure 1. The DICOM data were initially exported as compact disc-recordable (CD-R) in our hospital for security reasons (Fig.1a). The DICOM data on the CD-R were loaded and exported as a single file with the .dcm extension using Mango software (Multi-image Analysis graphical user interface, v4.1, available at https://mangoviewer.com/index.html) (Fig.1b). The DICOM data in the .dcm format were then imported into MATLAB R2023a (Fig.1c) using the Image Processing Toolbox. We utilized the Image Segmenter app in MATLAB (https://www.mathworks.com/help/images/ref/imagesegmenter-app.html). To manually segment ROI from the original DICOM images, we used the imageSegmenter function implemented in the Image Processing Toolbox (https://mathworks.com/help/images/image-segmentation-using-the-image-segmenter-app.html). To facilitate this segmentation process and improve efficiency, we mirrored the desktop displaying the Image Segmenter onto a tablet computer (in our case, a 12.9-inch iPad Pro 5th generation, Apple, Inc., Cupertino, CA). To obtain segmentation, we manually traced the ROI on the tablet computer screen using a stylus pen (in our case, Apple Pencil 2nd generation, Apple, Inc., Cupertino, CA) (Fig.1d). Representative intracranial segmentations from a single subject, along with the original CT images, are presented in Figure 2. Once all ROI segments were obtained (Fig.1e), we counted the total number of pixels in the segments and obtained volumetric values (in milliliter, mL) (Fig.1f). The segmentation process was performed by a single examiner (Hiroaki H). MATLAB code related to this procedure is provided in the Supplemental Data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVolume Calculation Using OsiriX\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe .dcm files generated by the method described above were imported into OsiriX. The OsiriX display was mirrored onto a tablet computer, where the ROI was manually traced using a stylus pen to obtain segmentation, following the same methodology described above. A single examiner (Hiroaki H) also performed the segmentation process. After ROI segmentation, OsiriX calculates the ROI volume (mL).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical Analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe generated two sets of volumetric data using our MATLAB-based method and Osirix, both derived from the same subject population and CT scans. For each subject, two different volumetric values were calculated, one from the MATLAB method and the other from OsiriX. Since these two data sets correspond to the same subjects, a paired t-test was performed to assess the differences between the two methods. This test evaluates whether the volumetric measurements obtained from the MATLAB method significantly differ from those obtained with OsiriX.\u003c/p\u003e\n\u003cp\u003eIn addition, an unpaired t-test was conducted to explore potential differences in volumetric data sets, treating them as if they were independent samples from the same population, obtained using two distinct methodologies. This approach tested whether the MATLAB method and OsiriX produced significantly different volumetric data sets.\u003c/p\u003e\n\u003cp\u003eStatistical significance was defined as p-values \u0026lt;0.05. Additionally, a power analysis was conducted to determine the effect size (Cohen\u0026rsquo;s d) and power (1 - beta).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eBaseline Characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 83 CT scans from pediatric patients with minor head trauma were analyzed according to the exclusion and inclusion criteria, with 33 females (39.8%). The mean age at the time of the CT scans was 66.55 \u0026plusmn; 30.72 months. The volumetric values calculated using our MATLAB method had a mean of 1292.68 \u0026plusmn; 128.02mL. The volumetric values calculated using OsiriX had a mean of 1288.60 \u0026plusmn; 128.03mL.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults from Paired t-test\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe compared the volumetric values calculated using our MATLAB method with those from OsiriX using a paired t-test. A significant difference was observed (p \u0026lt; 0.001, Fig. 3a), indicating that the volumetric values obtained using our MATLAB method were statistically different from those obtained using OsiriX. The difference between values obtained from our MATLAB method and those from OsiriX for each patient had a mean of 4.09 \u0026plusmn; 9.12 mL. The distribution of these differences is shown in Figure 3b. On average, the volumetric values from our MATLAB method were 4.09 mL higher than those from OsiriX. This corresponds to 0.32 % of the intracranial volume.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults from Unpaired t-test\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe distributions of volumetric values from our MATLAB method and OsiriX are presented in Figure 3c. An unpaired t-test revealed no statistically significant difference between the two groups (p = 0.84). This suggests that, when treated as independent samples, the volumetric groups produced by our MATLAB method and Osirix did not differ significantly.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePower Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the power analysis for the paired and unpaired t-tests used in this study are shown in Table 1. The power of the paired t-test was high (0.98), confirming that the volumetric values obtained using our MATLAB method were statistically different from those obtained using OsiriX. However, the power of the unpaired t-test was low (0.06), due to the small effect size (0.032). The sample size required for a power of 0.8 was estimated to be 7709. The beta error was calculated as 0.94 (1 \u0026ndash; 0.06), indicating that there is a high risk of incorrectly accepting the null hypothesis in the unpaired t-test. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eResults of power analysis.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"680\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6176%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.9118%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect size (Cohen\u0026rsquo;s d)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1471%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample size for 0.8 power\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3235%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePower (1 - beta)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6176%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePaired t-test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.9118%;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1471%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3235%;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6176%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnpaired t-test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.9118%;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.1471%;\"\u003e\n \u003cp\u003e7709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3235%;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFor both paired and unpaired t-test conditions, the effect size was calculated based on Cohen\u0026rsquo;s d, the required sample size to achieve a power of 0.8 was determined, and the power values were calculated.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study introduces a methodology for calculating intracranial volume from head CT DICOM data using custom MATLAB code, a tablet computer, and a stylus pen. To evaluate the accuracy of our MATLAB-based method, we compared it with an established method, OsiriX. The paired t-test revealed a significant difference between the two methods, with the volumetric values obtained using our MATLAB method being 0.32 % larger than those from OsiriX. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOsiriX is a well-established DICOM viewer [17], recognized for its user-friendly interface, ability to load detailed images efficiently, and the fact that it is both open-source and free of charge [18]. OsiriX has been widely used for volumetric assessments [19-21], which is why we selected it to evaluate the accuracy of our developed MATLAB-based method. Although we anticipated no significant difference between the results of our MATLAB method and OsiriX, the paired t-test revealed a statistically significant difference.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne potential source of this discrepancy is the manual segmentation of ROIs, which may have introduced slight variations between the areas segmented using our method and OsiriX. Since all segmentations in this study were performed by a single examiner (H. H.), the variation due to manual segmentation was consistent across all samples, and we believe that the effects of this variability were minimized by analyzing a larger sample size. Therefore, we infer that the observed statistical difference between the two methods is likely due to methodological differences rather than segmentation-related variation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile we cannot definitively explain the cause of this difference, we noticed that manually segmented ROIs tended to appear slightly smaller in OsiriX compared to our MATLAB method. This suggests that the measurements obtained using Osirix were, on average, 4.09 mL smaller than those obtained using our method. However, this remains an assumption. Given the average intracranial volume of 1292.68 mL obtained by our method, the difference represents a small proportion (0.32 %), leading us to conclude that this difference may be negligible for practical purposes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our previous research, we used this MATLAB-based method to calculate volumes of various structures. For example, using head MRI, we quantified lesion volumes on diffusion-weighted imaging, demonstrating differential outcomes between groups undergoing mechanical thrombectomy [22]. Similarly, using head CT, we measured chronic subdural hematoma volumes, identifying differences between recurrence and non-recurrence groups [23]. Additionally, we have published several studies investigating intracranial structural volumes in the Japanese pediatric population [5, 6, 24]. In this study, while we focused on intracranial volume using head CT images, our method can be applied to MRI DICOM data as well. Moreover, manual segmentation enables the measurement of regions beyond the intracranial volume. We suggest that readers consider using this method to segment various structures with CT or MRI DICOM data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eManual segmentation, as employed in our MATLAB method, requires additional time and effort. To mitigate this issue, we proposed using a stylus pen for tracing segmented lines on a tablet computer screen, which mirrors the desktop display. This approach has been reported previously [25], showing that stylus pen-based volumetric techniques offer a technological advancement over traditional mouse-based tracing by providing improved statistical accuracy and significantly reducing the time required [26]. Therefore, we recommend using our MATLAB method in combination with a tablet computer and a stylus pen to enhance efficiency and accuracy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study used a 5 mm slice width for volume calculation. However, CT images with a 1mm slice width are now widely used at our hospital, allowing for more accurate image evaluation. At our institution, head CT images are initially acquired with a 0.5 mm slice width and then reconstructed into 5 mm or 1 mm slices. While 5 mm slices are routinely reconstructed in all cases, 1mm slices are only reconstructed when necessary to avoid unnecessary procedures. Additionally, as 1 mm slice images contain five times more data than 5 mm slices, the labor and time required for manual segmentation would be significantly increased. Since the primary goal of this study was to compare our MATLAB method with OsiriX, we opted to use 5mm slice CT images to minimize the time and effort needed for segmentation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. Due to its retrospective nature, different CT protocols were used, and we were unable to ensure consistency across these protocols. Additionally, although we used 5 mm slice CT images, more accurate volumetric evaluations could be achieved with 1 mm slice images, which are preferable for precision.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe MATLAB-based methodology for calculating volume using DICOM images is presented, utilizing a tablet computer and stylus pen. While the volumetric values obtained using our method statistically differed from those calculated with Osirix, the difference was only 0.32%, which we consider negligible for practical purposes. The actual MATLAB code is provided in the Supplemental Data, allowing readers to implement our method easily. We hope that this study contributes to advancing your research efforts.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eCD-R\u003c/strong\u003e, compact disc-recordable; \u003cstrong\u003eCT\u003c/strong\u003e, computed tomography; \u003cstrong\u003eDICOM\u003c/strong\u003e, Digital Imaging and Communications in Medicine; \u003cstrong\u003eMATLAB\u003c/strong\u003e, MATrix LABoratory;\u003cstrong\u003e\u0026nbsp;mL\u003c/strong\u003e, milliliter;\u003cstrong\u003e\u0026nbsp;MRI,\u0026nbsp;\u003c/strong\u003emagnetic resonance imaging;\u003cstrong\u003e\u0026nbsp;ROI\u003c/strong\u003e, regions of interest.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.H. conceived the study, collected and analyzed the data, developed the MATLAB program, created all figures and tables, and was primarily responsible for writing the manuscript. M.S. collected the data. All\u0026nbsp;authors provided clinical care and evaluated the patients. O.T. and Y.C. supervised the study. All authors have reviewed the manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Japan Society for the Promotion of Science (JSPS) KAKENHI [JP21K16629 (Hiroaki Hashimoto)] supported this work.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are available from the corresponding authors upon reasonable request and after additional ethics approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Ethics Committee of Osaka Women\u0026rsquo;s and Children\u0026rsquo;s Hospital (Izumi, Japan, approval no. 1634), adhering to the Declaration of Helsinki. Informed consent was obtained through the opt-out method on our center\u0026rsquo;s website, considering the retrospective and noninvasive nature of the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors reviewed the complete manuscript and consented to publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no competing interests or conflicts of interest to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePrassopoulos P, Cavouras D, Golfinopoulos S (1996) Developmental changes in the posterior cranial fossa of children studied by CT. Neuroradiology 38: 80-83\u003c/li\u003e\n\u003cli\u003eCutler NS, Srinivasan S, Aaron BL, Anand SK, Kang MS, Altshuler DBet alKhalsa SSS (2020) Normal cerebral ventricular volume growth in childhood. J Neurosurg Pediatr 26: 517-524\u003c/li\u003e\n\u003cli\u003eAkar E, Kara S, Akdemir H, Kırış A (2017) 3D structural complexity analysis of cerebellum in Chiari malformation type I. Med Biol Eng Comput 55: 2169-2182\u003c/li\u003e\n\u003cli\u003eCalandrelli R, Panfili M, D\u0026rsquo;Apolito G, Zampino G, Pedicelli A, Pilato F, Colosimo C (2017) Quantitative approach to the posterior cranial fossa and craniocervical junction in asymptomatic children with achondroplasia. Neuroradiology 59: 1031-1041\u003c/li\u003e\n\u003cli\u003eHashimoto H, Takemoto O, Chiba Y (2023) Growth patterns and ratios of posterior cranial fossa structures in the Japanese pediatric population: a study utilizing CT scans. Neuroradiology 65: 1835-1844\u003c/li\u003e\n\u003cli\u003eHashimoto H, Takemoto O, Nishimoto K, Moriguchi G, Nakamura M, Chiba Y (2023) Normal growth curve of choroid plexus in children: implications for assessing hydrocephalus due to choroid plexus hyperplasia. J Neurosurg Pediatr 32: 627-637\u003c/li\u003e\n\u003cli\u003eKhalsa SSS, Siu A, DeFreitas TA, Cappuzzo JM, Myseros JS, Magge SNet alKeating RF (2017) Comparison of posterior fossa volumes and clinical outcomes after decompression of Chiari malformation Type I. J Neurosurg Pediatr 19: 511-517\u003c/li\u003e\n\u003cli\u003eSweeney KJ, Caird J, Sattar MT, Allcutt D, Crimmins D (2013) Spinal level of myelomeningocele lesion as a contributing factor in posterior fossa volume, intracranial cerebellar volume, and cerebellar ectopia. J Neurosurg Pediatr 11: 154-159\u003c/li\u003e\n\u003cli\u003eSgouros S, Goldin JH, Hockley AD, Wake MJ, Natarajan K (1999) Intracranial volume change in childhood. J Neurosurg 91: 610-616\u003c/li\u003e\n\u003cli\u003eSgouros S, Kountouri M, Natarajan K (2006) Posterior fossa volume in children with Chiari malformation Type I. J Neurosurg Pediatr 105: 101-106\u003c/li\u003e\n\u003cli\u003eTrigylidas T, Baronia B, Vassilyadi M, Ventureyra E (2008) Posterior fossa dimension and volume estimates in pediatric patients with Chiari I malformations. Childs Nerv Syst 24: 329-336\u003c/li\u003e\n\u003cli\u003eVurdem \u0026Uuml;E, Acer N, Ertekin T, Savranlar A, Inci MF (2012) Analysis of the volumes of the posterior cranial fossa, cerebellum, and herniated tonsils using the stereological methods in patients with Chiari type I malformation. Sci World J 2012\u003c/li\u003e\n\u003cli\u003eKamdar MR, Gomez RA, Ascherman JA (2009) Intracranial volumes in a large series of healthy children. Plast Reconstr Surg 124: 2072-2075\u003c/li\u003e\n\u003cli\u003eKamochi H, Sunaga A, Chi D, Asahi R, Nakagawa S, Mori Met alYoshimura K (2017) Growth curves for intracranial volume in normal Asian children fortify management of craniosynostosis. J Craniomaxillofac Surg 45: 1842-1845\u003c/li\u003e\n\u003cli\u003eColl G, Arnaud E, Collet C, Brunelle F, Sainte-Rose C, Di Rocco F (2015) Skull base morphology in fibroblast growth factor receptor type 2-related faciocraniosynostosis: a descriptive analysis. Neurosurgery 76: 571-583\u003c/li\u003e\n\u003cli\u003eColl G, Lemaire J-J, Di Rocco F, Barth\u0026eacute;l\u0026eacute;my I, Garcier J-M, De Schlichting E, Sakka L (2016) Human Foramen Magnum Area and Posterior Cranial Fossa Volume Growth in Relation to Cranial Base Synchondrosis Closure in the Course of Child Development. Neurosurgery 79: 722-735\u003c/li\u003e\n\u003cli\u003eRosset A, Spadola L, Ratib O (2004) OsiriX: An Open-Source Software for Navigating in Multidimensional DICOM Images. 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Vet Radiol Ultrasound 63: 729-738\u003c/li\u003e\n\u003cli\u003eChuang C-C, Lin S-Y, Pai P-C, Yan J-L, Toh C-H, Lee S-Tet alLee C-C (2017) Different Volumetric Measurement Methods for Pituitary Adenomas and Their Crucial Clinical Significance. Sci Rep 7: 40792\u003c/li\u003e\n\u003cli\u003eHashimoto H, Maruo T, Kimoto Y, Nakamura M, Fujinaga T, Nakamura H, Ushio Y (2023) The association between diffusion-weighted imaging-Alberta Stroke Program Early Computed Tomography Score and the outcome following mechanical thrombectomy of anterior circulation occlusion. Interdiscip Neurosurg 33: 101758\u003c/li\u003e\n\u003cli\u003eHashimoto H, Maruo T, Kimoto Y, Nakamura M, Fujinaga T, Ushio Y (2023) Burr hole locations are associated with recurrence in single burr hole drainage surgery for chronic subdural hematoma. World Neurosurg: X 19: 100204\u003c/li\u003e\n\u003cli\u003eHashimoto H, Irizato N, Takemoto O, Chiba Y (2024) Intracranial volumetric evaluation in postnatally repaired myelomeningocele infants. Childs Nerv Syst 40: 2851-2858\u003c/li\u003e\n\u003cli\u003eSalaffi F, Carotti M, Ciapetti A, Ariani A, Gasparini S, Grassi W (2013) Validity of a computer-assisted manual segmentation software to quantify wrist erosion volume using computed tomography scans in rheumatoid arthritis. BMC Musculoskelet Disord 14: 265\u003c/li\u003e\n\u003cli\u003ePerandini S, Faccioli N, Inama M, Pozzi Mucelli R (2011) Freehand liver volumetry by using an electromagnetic pen tablet: accuracy, precision, and rapidity. J Digit Imaging 24: 360-365\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"childs-nervous-system","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cnsy","sideBox":"Learn more about [Child's Nervous System](http://link.springer.com/journal/381)","snPcode":"381","submissionUrl":"https://submission.nature.com/new-submission/381/3","title":"Child's Nervous System","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Image segmentation, volumetric measurement, CT, stylus pen, tablet computer","lastPublishedDoi":"10.21203/rs.3.rs-5411523/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5411523/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e This study presents a MATrix LABoratory (MATLAB)-based methodology for calculating intracranial volumes from head computed tomography (CT) data and compares it with established methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e Regions of interest (ROI) were manually segmented on CT images using a stylus pen, facilitated by mirroring a computer desktop onto a tablet. The volumetric process involved three main steps: (1) calculating the volume of a single voxel, (2) counting the total number of voxels within the segmented ROI, and (3) multiplying this voxel count by the single-voxel volume. This method was applied to 83 pediatric head CT scans from patients with minor head trauma, and the volumetric results were compared with those obtained from OsiriX.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003eA paired t-test revealed a statistically significant difference (p \u0026lt; 0.001) between the volumes obtained with our MATLAB-based method and those from OsiriX. However, the volumes obtained by our method were only 0.32% higher than the OsiriX measurements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003eAlthough a statistically significant difference was found between our method and OsiriX, the discrepancy is minimal and practically negligible. The MATLAB code provided in this study may serve as a valuable tool for beginners in radiological research.\u003c/p\u003e","manuscriptTitle":"Methodology for Pediatric Head Computed Tomography Image Segmentation and Volumetric Calculation Using a Tablet Computer and Stylus Pen","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 14:25:43","doi":"10.21203/rs.3.rs-5411523/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-03T10:38:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-02T06:21:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-29T16:38:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3125591162777513874341654200322927337","date":"2024-11-28T13:34:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184590862571711859417545508796291059679","date":"2024-11-25T14:30:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-23T09:41:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-08T13:02:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-08T13:02:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Child's Nervous System","date":"2024-11-07T16:37:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"childs-nervous-system","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cnsy","sideBox":"Learn more about [Child's Nervous System](http://link.springer.com/journal/381)","snPcode":"381","submissionUrl":"https://submission.nature.com/new-submission/381/3","title":"Child's Nervous System","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cd61bf43-ed50-4f5c-94db-9be60fda2d18","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-30T16:00:24+00:00","versionOfRecord":{"articleIdentity":"rs-5411523","link":"https://doi.org/10.1007/s00381-024-06723-y","journal":{"identity":"childs-nervous-system","isVorOnly":false,"title":"Child's Nervous System"},"publishedOn":"2024-12-23 15:57:17","publishedOnDateReadable":"December 23rd, 2024"},"versionCreatedAt":"2024-12-16 14:25:43","video":"","vorDoi":"10.1007/s00381-024-06723-y","vorDoiUrl":"https://doi.org/10.1007/s00381-024-06723-y","workflowStages":[]},"version":"v1","identity":"rs-5411523","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5411523","identity":"rs-5411523","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}