Deep Learning-Assisted Segmentation of X-ray Images for Rapid and Accurate Assessment of Foot Arch Morphology and Plantar Soft Tissue Thickness

Deep Learning-Assisted Segmentation of X-ray Images for Rapid and Accurate Assessment of Foot Arch Morphology and Plantar Soft Tissue Thickness | 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 Article Deep Learning-Assisted Segmentation of X-ray Images for Rapid and Accurate Assessment of Foot Arch Morphology and Plantar Soft Tissue Thickness Xinyi Ning, Tianhong Ru, Jun Zhu, Li Chen, Xin Ma, Ran Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4409140/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract The morphological characteristics of the foot arch and the plantar soft tissue thickness are pivotal in assessing foot health, which is associated with various foot and ankle pathologies. By applying deep learning image segmentation techniques to lateral weight-bearing X-ray images, this study investigates the correlation between foot arch morphology (FAM) and plantar soft tissue thickness (PSTT), examining influences such as age, gender, health status, physical activity, and footwear habits. Specifically, we use the DeepLab V3 + network model to accurately delineate the boundaries of the first metatarsal, talus, calcaneus, navicular bones, and overall foot, enabling rapid and automated measurements of FAM and PSTT. A retrospective dataset containing 1,497 X-ray images is analyzed to explore associations between FAM, PSTT, and various demographic factors. Our findings contribute novel insights into foot morphology, offering robust tools for clinical assessments and interventions. The enhanced detection and diagnostic capabilities provided by precise data support facilitate population-based studies and the leveraging of big data in clinical settings. Biological sciences/Computational biology and bioinformatics/Image processing Biological sciences/Computational biology and bioinformatics/Machine learning Physical sciences/Physics/Biological physics Image segmentation big data analysis foot arch morphology plantar soft tissue X-ray images Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 Figure 19 Figure 20 1. Introduction In daily activities, the foot, as a biomechanical structure, bears the body weight and plays a crucial role in human locomotion. When standing or walking, the ground reaction force applied to the foot can reach 1.2 times body weight, escalating to 2.5 times during more strenuous activities like running and jumping, the distribution and magnitude of the plantar pressure significantly influence foot health. [ 1 ]. The human foot comprises mainly bones and soft tissues, and due to inherent skeletal morphology differences and the influence of external forces during movement, the foot's bony structure, ligaments, and plantar soft tissues (PST) undergo elastic deformation, collectively determining the distribution of plantar pressure. Different regions accumulate varying stress over time, resulting in distinct deformations, reflected specifically in changes in foot arch morphology (FAM) and plantar soft tissue thickness (PSTT) [ 2 ]. Consequently, alterations in FAM and PSTT further impact the force direction and balance, thereby affecting foot health [ 3 ]. Therefore, the characteristics of FAM and PSTT are two vital factors extensively attended to by clinicians. The FAM is defined by multiple indices and serves as a key indicator in clinical evaluation of various foot pathologies. For instance, the diagnosis of flatfoot and high-arch feet primarily relies on foot arch height [ 4 ], which is determined by the curvature of the longitudinal arch and transverse arch, and influences foot stability, shock absorption, and propulsion efficiency [ 5 ]. Radiographic imaging is usually used to evaluate the foot morphology under weight-bearing conditions when evaluating foot deformity clinically [ 6 ]. Particularly, weight-bearing lateral X-rays of the foot are considered the gold standard for diagnosing adult-acquired flatfoot deformity [ 7 ] and for assessing medial longitudinal arch characteristics [ 8 ]. The PST is subjected to the highest mechanical loads in the human body and has developed unique properties over time to accommodate these demands. These include wear resistance, pressure tolerance, and the limitation of interlayer displacement. Functionally, the PST supports weight, absorbs and cushions impacts from the ground, and maintains body balance and stability [ 9 , 10 ]. It consists of complex structures such as skin, adipocytes, fascial layers, and muscles [ 11 ]. Variations in PSTT can influence the distribution of plantar pressure and the overall biomechanical behavior of the foot. Clinical observations and statistical data indicate that PST degeneration is linked to many common foot and ankle disorders, particularly in elder adults. Such degeneration may lead to pain and contribute to conditions like metatarsalgia, plantar fasciitis, hallux valgus, and complications in diabetic foot conditions [ 12 – 18 ]. While FAM and PSTT are common indicators for evaluating foot health clinically, the evaluation methods exhibit subjectivity and lack standardized criteria. In clinical practice, doctors typically manually annotate and measure radiological imaging results to assess FAM, focusing on parameters such as the calcaneal pitch angle (CPA) and the talo-first metatarsal angle (TMA or Meary's angle) to diagnose conditions like flatfoot and high arches [ 19 ]. However, these angles can be measured using approximately four to six different methods [ 20 ], and there is no standardized approach for defining and measuring arch height. Additionally, these manual measurements are time-consuming for practitioners. Worse still, detecting variations in PSTT poses greater challenges. PSTT exhibits strong individualized characteristics, influenced by factors such as age, gender, diseases, and lifestyle [ 21 ][ 22 ], making standardized evaluation difficult. With the advancement of ultrasound technology, ultrasound has become the primary method for detecting PSTT [ 23 ][ 24 ][ 25 ]. Additionally, with the development of computer technology and advanced medical imaging techniques, studies employing visual image processing techniques, including deep learning and artificial intelligence, for detecting PSTT in foot medical imaging have been widely reported [ 26 ][ 27 ][ 28 ]. Although some literatures have conducted meta-analyses on deep learning image segmentation for PST, there is still a lack of analysis regarding the correlation between PSTT and factors such as gender, age, and footwear habits, especially based on large data samples for comprehensive statistical parameter information on PSTT. Furthermore, due to the difficulty in collecting medical data, previous studies have not analyzed the correlation between foot skeletal structure and PST based on large data volumes. Therefore, previous investigations have provided inconsistent and inconclusive results regarding the relationship between FAM and PSTT, without sufficient exploration into the impact of demographic factors such as gender and age on this relationship. However, it’s a meaningful task to explore the associations between FAM, PSTT, and different population categories, as well as their intrinsic correlations. This work is crucial for accurate and convenient foot health assessment and diagnosis, understanding foot biomechanical structures, disease prevention and rehabilitation, footwear and orthotic design, and foot modeling, etc. To address the aforementioned challenges, our study amassed a substantial dataset of weight-bearing lateral foot X-ray images, a type for which there is currently no publicly available dataset. A set of 1497 images is retrospectively collected from the foot and ankle database of Huashan Hospital (Shanghai, China) spanning the last decade, with the personal info anonymized and ethic review approved. Utilizing deep learning image segmentation techniques, we preprocessed these images by adjusting grayscale, removing noise, and normalizing the images, enhancing the model's robustness, stability, and accuracy [ 29 ]. We then trained a deep neural network to perform precise segmentation of the first metatarsal (FM), talus (TA), calcaneus (CA), navicular (NAVI) bones, as well as the overall foot boundary. This approach enabled automated, standardized, and batch processing for precise computations of FAM and PSTT, thereby yielding significant time and cost efficiencies. Furthermore, our research interest lies in analyzing the homogeneity and heterogeneity of large data samples, using data-driven approaches to discover patterns of similarity and dissimilarity among population groups. We further investigated the correlation between FAM and PSTT among different population groups. The section 2 describes the methodology, covering the data source, composition, and preprocessing of the dataset, as well as the development of deep learning image segmentation models and the evaluation metrics for FAM and PSTT. Section 3 presents the results, detailing the performance of the image segmentation models, the data results for FAM and PSTT, and the analysis of the correlation between FAM and PSTT across different demographic groups. Section 4 discusses the methodologies, results, and underlying hypotheses, followed by a summary and future perspectives of the study. The overall workflow of the study is illustrated in Fig. 1 . 2. Methods 2.1 Dataset The application of deep learning for image detection and segmentation requires a substantial dataset. Due to the unavailability of public X-ray image datasets, we undertook a retrospective data collection to facilitate efficient and cost-effective research. We compiled 1497 weight-bearing lateral full foot X-ray images from Huashan Hospital's foot and ankle imaging database, spanning from 2013 to 2022, involving 1098 patients. The data, stored in DICOM file format [ 30 ][ 31 ], includes demographic details such as gender, age, and imaging timestamps. All data samples were anonymized during processing and subsequent research phases to ensure privacy. Additionally, to account for the developmental stage of children’s skeletons, we excluded samples from individuals under the age of 14 years. The collection process also involved manual screening by foot and ankle surgeons to exclude images from patients with skeletal or soft tissue foot defects, a history of foot ulcers, neurological joint diseases, post-foot surgery conditions, and those unable to walk independently. The X-ray images were sourced from medical imaging devices produced by several manufacturers, including GE, Canon, Philips, CARESTREAM, and KODAK. These devices capture images with an average pixel spacing of approximately 0.14 mm/pixel. The X-rays are collected as grayscale images with a depth of 16 bits, and the average resolution of the collected images is 2507×2080 pixels. For visualizing and processing the X-ray images, we employed the PyDicom library, a medical image processing tool, to parse DICOM files and convert the X-ray grayscale images into JPG format for easier handling [ 32 ]. Table 1 presents the basic information of these data samples. In this study, each X-ray image is treated as an individual data sample. This includes both left and right foot X-ray images of the same patient and multiple images taken from the same patient over the past decade, without filtering for duplicate individuals in the dataset. Table 1 Statistical information of data samples 2.2 Image preprocessing From the dataset, 220 images were randomly selected and divided into training, validation, and testing sets, with 180, 20, and 20 images respectively. Under the supervision of foot and ankle surgeons, these images were manually annotated for precise boundary delineation of the entire foot and the four bone structures: FM, TA, CA, and NAVI, using the LabelMe library. These annotations served as the ground truth for model training [ 33 ]. Once the model's accuracy and generalization were confirmed, it was applied to all sampled images to automatically calculate metrics related to FAM and PSTT. This facilitated large-scale data analysis to investigate the factors influencing these measurements. Additionally, to enhance the robustness and generalization capability of the model, we employed the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm for contrast enhancement [ 34 ], and converted the 16-bit X-ray image into 8-bit images with sufficient contrast. Subsequently, image normalization is performed to reduce differences in brightness and contrast, mitigating the model's susceptibility to outliers or extreme pixel values. Next, in order to reduce computational complexity and memory usage, we utilized the bilinear interpolation method [ 35 ] for resizing the original images to a unified size of 384×576 pixels as input for the model, and ensured that the key semantic information in the images was preserved. Figure 2 illustrates an example weight-bearing lateral foot X-ray image, where (a) is the original grayscale image parsed from the DICOM file, serving as the input to the model, (b) displays the manual labeling results using the Labelme library, stored as a JSON file, while (c-g) illustrate the boundaries of the entire foot, FM, TA, CA and NAVI bones obtained from parsing the JSON file. Here, for visual clarity, these label boundaries are overlaid on the original image for visualization, though each labeled image is actually a binary black-and-white image. And (h) shows the visualization of various labeled images with different pixel values overlaid on one image. 2.3 Deep learning image segmentation model In our selection of deep learning network models for the task of calcaneus (CA) segmentation, we evaluated four widely used models in medical image segmentation: FCN [ 36 ], U-Net [ 37 ], SegNet [ 38 ], and DeepLab V3+ [ 39 ]. Among these, DeepLab V3 + demonstrated superior performance, particularly in detailed boundary segmentation and generalization capabilities. Consequently, we chose the DeepLab V3 + model for automatic image segmentation. To enhance robustness and accuracy, we constructed five independent DeepLab V3 + segmentation models, one for the entire foot boundary and one for each of the four bone boundaries (FM, TA, CA, NAVI). Each model was trained separately to optimize parameters. The input image dimensions were standardized to 384x576 pixels, and outputs were binarized using the sigmoid function [ 40 ]. To ensure reproducibility, all training runs were conducted with fixed seed settings. PyTorch was used for model construction and training, with parameters as follows: Adam optimizer [ 41 ], learning rate is set to 10 − 4 , batch size is set to 4, epoch is set to 20. The environment and versions are macOS Ventura 13.2.1, 4-cores CPU, 16GB RAM, PyTorch version 1.8. For the loss function and evaluation metrics, we selected the Dice coefficient and Intersection over Union (IoU). The Dice coefficient is particularly sensitive to small targets, making it ideal for precise segmentation of smaller anatomical structures, while IoU is well-suited for large target detection and segmentation tasks. Therefore, we utilized Dice loss for training to optimize our model's ability to detect small variations, and employed IoU as the evaluation metric to assess the overall accuracy and integrity of the segmentation across larger areas [ 42 ]. Additionally, in the test set, in rare extreme cases where X-ray images contained high-intensity artifacts, the model might misclassify noise and contamination during segmentation. Therefore, post-processing was applied to the segmentation masks using the DBSCAN algorithm for clustering [ 43 ]. This step retained the largest clustered area as the target region and set the values of smaller misclassified noise regions to 0, eliminating interference in subsequent tasks such as extracting bone axes and calculating PSTT. 2.4 Calculation and evaluation of FAM and PSTT indicators In this study, we focused on three primary descriptors of FAM as advised by foot and ankle surgeons: the angle between the axes of the first metatarsal and the talus (“angle-fm-ta”), the inclination of the calcaneus axis relative to the plantar surface (“angle-ca-plantar”), and the longitudinal arch height (LAH). Additionally, we measured PSTT at the forefoot and rearfoot regions. To calculate the “angle-fm-ta” and “angle-ca-plantar” in weight-bearing lateral foot X-ray images, we first applied the Principal Component Analysis (PCA) algorithm [ 44 ] to determine the principal axes of the segmented FM, TA, and CA bones. We then calculated the angle between the principal axes of the FM and TA to determine the “angle-fm-ta.” This method mirrors the standardized manual angle measurements performed by surgeons using X-ray reading software, reducing subjective variability. The “angle-ca-plantar” was defined as the angle between the main axis of the CA and the horizontal plane, as suggested by surgeons. For the calculation of LAH, we identified the center of the NAVI bone based on the PCA algorithm and defined it as the distance from the NAVI bone center to the median of the PST boundary points on the forefoot and rearfoot. Figure 3 displays schematic diagrams of these measurements for both the left (a) and right feet (b). Notably, here we stipulated that the “angle-fm-ta” is the angle between the FM axis and the TA axis, potentially resulting in angles greater than 180°. We also measured PSTT by calculating the distance from the lowest boundary point of the FM to the foot's lower border directly beneath it, denoted as the forefoot PSTT (arrow A in Fig. 3 ). Similarly, the rearfoot PSTT was measured from the lowest point of the CA to the foot boundary beneath it (arrow B in Fig. 3 ). For comparative analysis, we calculated the foot length (FL), defined as the distance between the outermost points of the toe and heel, marked by a red line in Fig. 3 . The LAH and PSTT values were then normalized by dividing by the FL, resulting in normalized indicators: normalized LAH, normalized forefoot PSTT, and normalized rearfoot PSTT. 3. Results 3.1 Image segmentation model results Table 2 summarizes the performance of five segmentation model on the training, validation, and test set respectively. It is observed that among the five segmentation tasks, the performance of the entire foot segmentation model is the best. Besides, all models demonstrate good performance and generalization, thereby avoiding the issue of overfitting. Table 2. Accuracy of DeepLab V3 + Image Segmentation Models Figure 4 showcases the automated segmentation results of a randomly selected image from the testing set. Figure 4 (b-f) illustrate the segmentation outcomes for the entire foot boundary, and the FM, TA, CA, and NAVI bone regions, respectively. These results highlight precise delineation of the foot outline and the CA boundaries, demonstrating the model's effectiveness in these areas. However, there are minor discrepancies in the segmentation of the FM, TA, and NAVI bones, primarily due to the challenges inherent in automatically segmenting these complex structures. Similar to the manual annotation process, which even experienced foot and ankle surgeons find time-consuming and demanding in terms of precise boundary positioning, the segmentation of these smaller and more intricate boundaries presents considerable challenges for the models. Despite these challenges, the performance of the models is relatively satisfactory. In the test set, foot and ankle surgeons manually reviewed and validated the segmentation results, noting a high overlap rate with the actual bone boundaries. These minor discrepancies were deemed to have negligible impact on subsequent computations and analyses of FAM and PSTT, confirming the model's utility and accuracy. Furthermore, Fig. 5 demonstrates the comparative effectiveness of using the DBSCAN method for handling outlier data with artifacts. It is visually apparent that this method effectively rectifies errors in the DeepLabV3 + model. 3.2 Result of FAM analysis A. Distribution of FAM characteristic indicators Figure 6 (a), (b), and (c) show the statistical histogram of the angle-fm-ta, angle-ca-plantar, and the normalized LAH respectively, obtained through automated calculations via image segmentation and PCA. It is evident from the figures that the distribution of the angle-fm-ta is mainly concentrated around 180° (182.151° ± 11.433°), which is consistent with the normal FAM in medicine. The angle-ca-plantar is mainly distributed around 12° (11.941° ± 6.169°), but a small number of negative values are observed, which were subsequently diagnosed by orthopedic surgeons as cases of flatfoot during further analysis. The mean and standard deviation of the normalized LAH are calculated as 0.214 times FL and 0.034 times FL, respectively. Analysis of the coefficient of variation (angle-fm-ta CV = 0.063, angle-ca-plantar CV = 0.517, normalized LAH CV = 0.160) for the three metrics indicates that the angle-fm-ta exhibits the highest stability across various samples. B. Interrelationships among FAM characteristic indicators As shown in Fig. 7 , there is a strong correlation among the three characteristic indicators of FAM. The normalized LAH exhibits a negative correlation with the angle-fm-ta, while showing a positive correlation with the angle-ca-plantar. Subjects with larger angle-fm-ta tend to have smaller angle-ca-plantar, and may even become negative in some cases. Additionally, their normalized LAH tends to be lower. Such characteristics are associated with flatfoot conditions. C. Relationship between FAM indicators and gender Figure 8 illustrates the variations in the distributions of angle-fm-ta, angle-ca-plantar, and normalized LAH, highlighting the influence of gender on these metrics. The data shows that the angle-fm-ta is generally higher in females than in males. Additionally, there is a strong correlation among these three indicators; correspondingly, the angle-ca-plantar and normalized LAH are both observed to be smaller in females compared to males. This pattern underscores the impact of gender-specific anatomical differences on these foot arch morphology metrics. D. Relationship between FAM and age We categorized the age distribution of all collected samples into eight groups, ranging from 14 to 90 years old, with the following brackets: [ 14 – 20 ], [ 21 – 30 ], [ 31 – 40 ], [41–50], [51–60], [61–70], [71–80], and [81–90]. This organization was made after excluding samples from individuals under the age of 14. Figure 9 (a-c) displays the box plots for the distribution of three evaluation indicators of (FAM) across these age groups. The plots indicate that there is no significant correlation between FAM and age, as the data distribution shows minimal variation among the different age categories. 3.3 Result of PSTT analysis A. Relationship between PSTT and gender Table 3 records the distribution of PSTT for all data samples, categorized by gender. The mean PSTT reported in the table represents the average thickness at both the forefoot and rearfoot. The data reveal a notable difference in PSTT between these two regions, with an average discrepancy of approximately 0.5mm. This measurement was specifically chosen to compare PSTT below the FM and below the CA. The observed variability between these areas provides a foundation for further investigation into regional differences in PSTT across the foot. The distribution of forefoot and rearfoot PSTT normalized by foot length among different genders is shown in Fig. 10 . The results indicate that the PSTT of males is thicker than that of females, especially with a more pronounced difference in the forefoot PSTT. Table 3. The average result of plantar soft tissue thickness (with grouping by gender) B. Relationship between PSTT indicators and age Table 4 summarizes the results of PSTT grouped by age. Due to the limitation of retrospective data collection, it is not feasible to subjectively intervene in achieving a balanced distribution of sample sizes within each age group. Table 4. The average result of plantar soft tissue thickness (with grouping by age) Figure 11 (a) and (b) provide a clearer and more intuitive depiction of how normalized forefoot and rearfoot PSTT varies with age within each group. From these figures, it is evident that PSTT at both the forefoot and rearfoot follows a similar trend with age, initially increasing and then decreasing as age progresses. This trend can be partly attributed to the fact that adolescents in the [ 14 – 20 ] age group are still undergoing developmental changes, which may influence measurements due to factors like skeletal growth, height, and changes in foot length. Additionally, the sample size for the [81–90] age group is only 18, significantly smaller than other age groups, making the data susceptible to outliers and potentially distorting the overall results. After excluding data from the [ 14 – 20 ] and [81–90] age groups, the trend of decreasing PSTT in older age groups becomes more pronounced, indicating that the elderly tend to have thinner plantar soft tissues compared to those in the middle-aged group, with more noticeable changes particularly in the heel area. Furthermore, when examining the impact of age on PSTT, we also considered the influence of gender. Figure 12 displays the data distribution and performance results of mean PSTT for males and females within each age group, aligning with the gender influence trends discussed earlier. Across most age groups, except for the [81–90] group, the PSTT for males is generally greater than that for females. Furthermore, the trend across age groups for both genders shows an initial increase in PSTT, followed by a decrease as age advances. This pattern highlights the nuanced interplay between age and gender in influencing PSTT. C. Relationship between PSTT and era The longitudinal trends and cross-sectional differences in PSTT over the past decade with gender and age has also been investigated. Despite the uneven distribution of sample numbers across different years, it is still feasible to conduct longitudinal comparative analyses to explore the impact of generational changes on PSTT, while holding gender and age as constant influencing factors. Figure 13 illustrates the temporal distribution of PSTT for males and females. Notably, there is a discernible decreasing trend in forefoot PSTT among females in recent years, comparing to the slight increase in male. It may hypothesize that this trend may be attributed to the fashion change in shoe wearing habit, such as high-heel, among modern women compared to earlier generations, however further investigations and evidences are necessary to approach the conclusion. Figure 14 illustrates the mean value of PSTT within age groups from 2013 to 2022, with (a) and (b) representing the data performance of forefoot and rearfoot PSTT, respectively. In years with fewer data samples, certain age groups lack data support, for instance, only three age groups have data for 2014. Despite excluding the sparse data from 2014, our current analysis has not revealed significant impacts of era differences on PSTT. This lack of clear trends may stem from the challenges associated with accounting for variations in potential influencing factors both within and between groups, such as weight, BMI, footwear habits, and activity levels, given the limited scale of the available data. Consequently, future research should focus on gathering a larger dataset or designing studies with more stringent controls over these variables to facilitate a more in-depth exploration of the influences on PSTT.. 3.4 Correlation between FAM and PSTT The foot arch and PST both serve critical functions in supporting body weight, bearing pressure, and cushioning impact forces. Their interactions influence each other, leading to changes in FAM and PSTT. Based on clinical advice, we examined the association between LAH and PSTT, the correlation between the angle-fm-ta and forefoot PST degeneration, and the relationship between the angle-ca-plantar and rearfoot PST. Figure 15 presents the data distribution and a linear regression analysis examining the relationship between LAH and PSTT across different genders. The PSTT is considered as an overall mean value of both forefoot and rearfoot, with a brown line representing the linear regression fit for the entire data sample, irrespective of gender. It indicates that individuals with lower arches, particularly those with flat feet, tend to experience increased load on the PST, making the soft tissues more susceptible to degeneration and consequently resulting in relatively thinner PST. Regarding the study relationship between the angle-fm-ta and forefoot PST degeneration, the correlation grouped by genders are illustrated in Fig. 16 . The bold blue line represents the overall data correlation. The overall trends for either males or females show a negative correlation. For samples with larger angle-fm-ta (greater than 180°), indicating with a flatter foot arch, there is a greater impact on the degeneration of the forefoot PST, resulting in a smaller forefoot PSTT. Following this, we explored the relationship between theangle-ca-plantar and rearfoot PSTT. Our analysis revealed that the impact of the angle-ca-plantar on the degeneration of rearfoot soft tissues is significantly correlated with the variation in thickness between the forefoot and rearfoot soft tissues. As illustrated in Fig. 17 , the term “ca-fm PSTT diff” refers to the normalized rearfoot PSTT minus the normalized forefoot PSTT. The figure demonstrates a negative correlation between the angle-ca-plantar and this thickness differential, with the data for females showing greater variability in these normalized thickness differences. A larger angle-ca-plantar suggests a heightened burden on the rearfoot soft tissues, which accelerates their degeneration relative to the forefoot. This results in a smaller disparity in thickness between the forefoot and rearfoot soft tissues. Similarly, we conducted statistical analysis on the age groups of data samples, considering the influence of gender on the above three types of correlation within different age groups. The results are illustrated in Figs. 18 , 19 , and 20 , respectively. It can be observed that, apart from the relatively small number of male samples in the [71, 80] age group and the extremely small sample size in the [81, 90] group, the correlation trends in other groups are generally consistent with the overall data correlation trends analyzed above, with minimal inter-group differences in performance. Additionally, in the age group [ 14 , 20 ], there are more outliers that deviate from the fitted curve. We attribute this phenomenon mainly to the fact that adolescents are still in their growth phase, and their bones are relatively weak in terms of accumulating long-term stress and undergoing structural changes, thus the data may be influenced by factors such as skeletal development, height, and foot length changes. 4. Discussion In this study, the absence of publicly available datasets necessitated manual annotation to construct a dataset for training deep learning models. Manual annotation of medical X-ray images, particularly for foot and ankle structures, involves significant cost and effort. For foot and ankle surgeons, accurately delineating the boundaries of the FM, TA, CA, and NAVI bones in a single lateral foot X-ray image is a labor-intensive and time-consuming task. A total of 220 images were randomly selected from all available samples for use in the segmentation models. This number was chosen based on the high cost associated with data annotation and the satisfactory model performance observed using 180 images for training. Additionally, although our dataset was constructed through random sampling, a small proportion of the images containing noise artifacts is inevitably not included in the model training, which could potentially affect the model's output accuracy, necessitating the use of post-processing methods like DBSCAN to correct errors. Furthermore, five DeepLab V3 + segmentation models were developed, each dedicated to segmenting the entire foot boundary and the four specific bone regions (FM, TA, CA, NAVI). The performance of each model varied, largely due to the distinct complexity associated with each segmentation task. Segmenting the entire foot boundary was relatively straightforward due to its prominent and distinct features. However, segmenting the boundaries of individual bones posed greater challenges because of their interconnected and overlapping feature in lateral X-ray images. Despite these complexities, the models generally produced satisfactory results. Moreover, manual annotation of bone boundaries in lateral foot X-rays presents significant challenges due to the three-dimensional skeletal structures being projected onto a two-dimensional plane, resulting in overlaps. Particularly challenging is the annotation of the talus, where both the medial and lateral surfaces of the upper part of the talus neck are visible and overlap due to varus and valgus deformities. The selection of which part to define as the boundary of the talus significantly impacts the calculation of its main axis. After extensive experimentation with various labeling methods, foot and ankle surgeons determined that the most accurate approach is to select the lower boundary of the projection of the talar roof as the upper edge, and the talocalcaneal articular surface as the lower edge, ignoring any lateral protrusions. This method ensures that the main axis results extracted by PCA align more closely with the actual anatomical structure. Another aspect that merits discussion involves the evaluation indicators for FAM and PSTT. Clinically, the talus-first metatarsal angle (also known as Meary's angle) in a lateral standing position typically sees the inferior talus oblique line extending through and being collinear with the first metatarsal axis, forming an angle of 0°, or slightly lower than the first metatarsal axis. The definition of when the talus-first metatarsal angle is positive or negative remains controversial, however in this work, the angle is specified as 180° for the collinear case. Furthermore, clinical evaluations of FAM often focus more on the inclination of the calcaneus along its superior oblique line rather than the inclination angle of the calcaneus axis (angle-ca-plantar). For clarity and simplicity in our analysis, and to better understand the trends and correlations with various factors, we opted to use the calcaneal axis inclination angle as our evaluation index. The superior calcaneal oblique line is typically drawn between two points: the first along the inferior surface of the calcaneocuboid joint and the second along the anteroinferior aspect of the medial tubercle. Regarding the PSTT, the forefoot primarily bears the support and balance function, while the rearfoot mainly provides support and propulsion to maintain body balance and stability [ 45 ]. Therefore, the load-bearing and degenerative conditions of PST differ across different locations. In lateral foot X-rays, we artificially defined the PSTT for the forefoot and rearfoot. Further detailed studies on the similarities and differences of PST at different locations may require a more nuanced distinction of PST regions. Lastly, another limitation of this study stems from the challenges associated with collecting medical imaging data, which is inherently time-consuming, costly, and involves sensitive information. Our retrospective approach to data collection introduced difficulties in controlling for variables such as gender, age, and era distributions, and limited our ability to obtain sensitive physiological and clinical data beyond basic demographics. Critical variables such as weight, BMI, footwear habits, activity levels, and medical history, which might reveal intergroup differences, were not available in the current dataset. Due to these constraints in data collection, certain subgroup analyses, particularly those investigating the impacts of age and era, may suffer from insufficient sample sizes to yield conclusive results. While we analyzed the potential influence of different eras on each investigated correlation, the uneven distribution of samples and the possibility that the existing volume of data may still be inadequate meant that not all analyses could be included in the results section. Consequently, we cannot definitively claim that era has no impact on FAM and PSTT. Looking ahead, analyzing a larger and more diverse dataset could lead to more comprehensive and robust conclusions. Additionally, for some of the relationships currently identified, such as the influence of gender on plantar soft tissue thickness, it remains challenging to determine whether differences between male and female groups in other variables might affect these outcomes. Thus, designing further single-variable experiments is necessary to validate our current hypotheses and provide a clearer understanding of these relationships. 5. Conclusion This study retrospectively collected weight-bearing lateral foot X-ray image data over the past decade, and devised a method based on deep learning image segmentation model to automatically extract FAM and PSTT from X-ray images accurately. By manually annotating and constructing the training dataset, DeepLabV3 + network model was trained to achieve precise segmentation of the FM, TA, CA and NAVI bone structures, and the entire foot boundary region. The PCA method was employed to extract the principal axes of bones to obtain the angle evaluation indicators for FAM. Compared to traditional manual measurements, this method not only enhances detection efficiency but also ensures accuracy and objectivity. It provides a consistent and reliable standardized quantitative method, laying a foundation for establishing big data correlation analysis of FAM and PSTT. Furthermore, based on the data samples and considering population differences and various features, correlation analyses and studies were conducted. Changes and variations in FAM and PSTT influenced by gender, age, and era were analyzed. Next, attempts were made to explore the correlation analysis between FAM and PSTT, as well as discussions based on gender and age differences. The overall trend of correlation within different gender and age groups was the same, and there was no significant difference. Admittedly, the dataset used in this study was retrospectively collected, making it difficult to control the balance of data quantities within gender, age, and era groups. Furthermore, it was not possible to obtain more clinical information and sensitive personal information, making it impossible to fully eliminate differences in other influencing factors within each group. Therefore, the hope lies in the collection of more sample data in future research and the design of more rigorous single-variable experiments to reduce inter-group interference. Subsequent analysis of more samples in future studies holds promise for arriving at more comprehensive and robust conclusions. However, the method introduced in this work, which explores the correlation and influencing factors of FAM and PSTT using deep learning and data-driven approaches, along with the current research results, provides a potential valuable foundation for further theoretical and practical explorations, and can serve as an automated method for evaluating flatfeet or high arched feet. The correlation and analysis studies between FAM, PSTT, and different influencing factors conducted in conjunction with population differences offer new perspectives on related studying. This work establishes a robust tool for clinical research and offers significant guidance for developing personalized interventions for foot-related diseases. Additionally, it has implications for optimizing footwear design to enhance foot health and performance, making it a valuable resource for both medical and industry applications. Declarations Competing interests: The authors declare no competing interests. Author Contributions Statement X.M. supervised and coordinated project; R.H., L.C. and J.Z. designed research; L. C. directed clinical trials, X.N. and T.R. performed research; X.N. analyzed data; and R.H. and X.N. wrote paper. Author Contribution X.M. supervised and coordinated project; R.H., L.C. and J.Z. designed research; L. C. directed clinical trials, X.N. and T.R. performed research; X.N. analyzed data; and R.H. and X.N. wrote paper. Acknowledgement This work is financially supported by the National Key Research and Development Program China (2022YFC2009500), the Medical Engineering Fund of Fudan University (YG2021-005, YG2022-008), the Fudan-Yiwu Fund (FYX-23-102), and the TZI-ZJU Industrial Program (2023CLG01, 2023CLG01PT). Data Availability • The main data generated or analyzed to support the conclusion during this study are included in this published article and its supplementary information files.• The full datasets generated and/or analyzed during the current study are not publicly available due PRIVACY PROTECTION POLICY AND ETHIC REQUIERMENT but are available from the corresponding author on reasonable request. References Keller, T. S., Weisberger, A. M., Ray, J. L., Hasan, S. S., Shiavi, R. G., & Spengler, D. M. (1996). Relationship between vertical ground reaction force and speed during walking, slow jogging, and running. Clinical biomechanics, 11(5), 253-259. San Tsung, B. Y., Zhang, M., Fan, Y. 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Gait & posture, 61, 238-242. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted Reviews received at journal 13 Jun, 2024 Reviews received at journal 29 May, 2024 Reviewers agreed at journal 21 May, 2024 Reviewers agreed at journal 20 May, 2024 Reviewers invited by journal 20 May, 2024 Editor assigned by journal 20 May, 2024 Editor invited by journal 14 May, 2024 Submission checks completed at journal 14 May, 2024 First submitted to journal 12 May, 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-4409140","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":305078245,"identity":"dc429cf1-ee33-4d3f-984b-ea1817aa4835","order_by":0,"name":"Xinyi Ning","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Ning","suffix":""},{"id":305078246,"identity":"3b9c3ac2-0081-4e65-81a9-c213c0c0d433","order_by":1,"name":"Tianhong Ru","email":"","orcid":"","institution":"Fudan 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(a) normalized forefoot PSTT, (b) normalized rearfoot PSTT, (c) normalized overall PSTT\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4409140/v1/7e5e559be784ce3c21e9e3d2.png"},{"id":57427793,"identity":"41ef7ac2-aed5-460f-9f62-3d1f78716143","added_by":"auto","created_at":"2024-05-30 14:35:39","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":91842,"visible":true,"origin":"","legend":"\u003cp\u003eData distribution on the relationship between PSTT and gender, (a) forefoot and (b) rearfoot\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-4409140/v1/bfd3fc85623126643fdc7866.png"},{"id":57427786,"identity":"972bfe8a-9218-4177-9e1f-a061bda403ad","added_by":"auto","created_at":"2024-05-30 14:35:38","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":70189,"visible":true,"origin":"","legend":"\u003cp\u003eBox plot of data distribution on the relationship between overall PSTT and gender with age\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-4409140/v1/7aa6bb5662e23095199fbf04.png"},{"id":57427792,"identity":"f545904c-a09f-4a31-9193-1c92e2a971e3","added_by":"auto","created_at":"2024-05-30 14:35:39","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":164777,"visible":true,"origin":"","legend":"\u003cp\u003e(a). Box plot of data distribution on the relationship between forefoot PSTT and era with gender, (b). Box plot of data distribution on the relationship between rearfoot PSTT and era with gender\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-4409140/v1/3a174327fdf5997c5fda1737.png"},{"id":57427787,"identity":"02d05bf8-f208-4cad-89b2-2e9b2d4019ff","added_by":"auto","created_at":"2024-05-30 14:35:38","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":122107,"visible":true,"origin":"","legend":"\u003cp\u003eThe mean PSTT values within age groups from 2013 to 2022\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-4409140/v1/c34a7dd0b4f6e435ce9fd244.png"},{"id":57428910,"identity":"51c226f2-03c0-42c9-93e0-7f7f5431f9b4","added_by":"auto","created_at":"2024-05-30 14:43:39","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":301248,"visible":true,"origin":"","legend":"\u003cp\u003eThe data distribution and linear regression between LAH and overall PSTT (grouped by gender)\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-4409140/v1/13eb73581b12ced9f1e52a8e.png"},{"id":57427796,"identity":"1cfa7df0-222a-4e56-bd9d-38cc27c1d0b7","added_by":"auto","created_at":"2024-05-30 14:35:39","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":341918,"visible":true,"origin":"","legend":"\u003cp\u003eThe data distribution and linear regression between angle-fm-ta and forefoot PSTT (by gender)\u003c/p\u003e","description":"","filename":"16.png","url":"https://assets-eu.researchsquare.com/files/rs-4409140/v1/19ed1142d33666dcafaebebd.png"},{"id":57427799,"identity":"45871513-d4e9-404e-88cf-f47b3f8c1e6b","added_by":"auto","created_at":"2024-05-30 14:35:39","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":251541,"visible":true,"origin":"","legend":"\u003cp\u003eThe data distribution and linear regression between angle-ca-plantar and ca-fm PSTT diff (by gender)\u003c/p\u003e","description":"","filename":"17.png","url":"https://assets-eu.researchsquare.com/files/rs-4409140/v1/6334f7117d4be4478fc63c61.png"},{"id":57427789,"identity":"256531be-d6b5-4ad2-86a3-a000b07be863","added_by":"auto","created_at":"2024-05-30 14:35:38","extension":"png","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":148625,"visible":true,"origin":"","legend":"\u003cp\u003eThe data distribution and correlation between LAH and overall PSTT (by gender and age)\u003c/p\u003e","description":"","filename":"18.png","url":"https://assets-eu.researchsquare.com/files/rs-4409140/v1/b20990dedb01a177f22ba513.png"},{"id":57428907,"identity":"c43c8242-ddff-4931-8df6-946068b0c1ed","added_by":"auto","created_at":"2024-05-30 14:43:38","extension":"png","order_by":19,"title":"Figure 19","display":"","copyAsset":false,"role":"figure","size":100188,"visible":true,"origin":"","legend":"\u003cp\u003eThe data distribution and correlation between angle-fm-ta and forefoot PSTT (by gender and age)\u003c/p\u003e","description":"","filename":"19.png","url":"https://assets-eu.researchsquare.com/files/rs-4409140/v1/62e7d1cc98b7d110f5bc0c58.png"},{"id":57427794,"identity":"338aa37c-39b3-4b72-a174-82a7295ca683","added_by":"auto","created_at":"2024-05-30 14:35:39","extension":"png","order_by":20,"title":"Figure 20","display":"","copyAsset":false,"role":"figure","size":113794,"visible":true,"origin":"","legend":"\u003cp\u003eThe data distribution and correlation between angle-ca-plantar and ca-fm PSTT diff (by gender and age)\u003c/p\u003e","description":"","filename":"20.png","url":"https://assets-eu.researchsquare.com/files/rs-4409140/v1/d26150c1eebf0c9e9abc1893.png"},{"id":63820962,"identity":"09e5a1db-5cdf-4a3e-9c7b-287386e03196","added_by":"auto","created_at":"2024-09-02 16:10:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4670824,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4409140/v1/f05a8732-4d82-41b3-aa01-c08977913dca.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep Learning-Assisted Segmentation of X-ray Images for Rapid and Accurate Assessment of Foot Arch Morphology and Plantar Soft Tissue Thickness","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn daily activities, the foot, as a biomechanical structure, bears the body weight and plays a crucial role in human locomotion. When standing or walking, the ground reaction force applied to the foot can reach 1.2 times body weight, escalating to 2.5 times during more strenuous activities like running and jumping, the distribution and magnitude of the plantar pressure significantly influence foot health. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The human foot comprises mainly bones and soft tissues, and due to inherent skeletal morphology differences and the influence of external forces during movement, the foot's bony structure, ligaments, and plantar soft tissues (PST) undergo elastic deformation, collectively determining the distribution of plantar pressure. Different regions accumulate varying stress over time, resulting in distinct deformations, reflected specifically in changes in foot arch morphology (FAM) and plantar soft tissue thickness (PSTT) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Consequently, alterations in FAM and PSTT further impact the force direction and balance, thereby affecting foot health [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, the characteristics of FAM and PSTT are two vital factors extensively attended to by clinicians.\u003c/p\u003e \u003cp\u003eThe FAM is defined by multiple indices and serves as a key indicator in clinical evaluation of various foot pathologies. For instance, the diagnosis of flatfoot and high-arch feet primarily relies on foot arch height [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], which is determined by the curvature of the longitudinal arch and transverse arch, and influences foot stability, shock absorption, and propulsion efficiency [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Radiographic imaging is usually used to evaluate the foot morphology under weight-bearing conditions when evaluating foot deformity clinically [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Particularly, weight-bearing lateral X-rays of the foot are considered the gold standard for diagnosing adult-acquired flatfoot deformity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and for assessing medial longitudinal arch characteristics [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe PST is subjected to the highest mechanical loads in the human body and has developed unique properties over time to accommodate these demands. These include wear resistance, pressure tolerance, and the limitation of interlayer displacement. Functionally, the PST supports weight, absorbs and cushions impacts from the ground, and maintains body balance and stability [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It consists of complex structures such as skin, adipocytes, fascial layers, and muscles [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Variations in PSTT can influence the distribution of plantar pressure and the overall biomechanical behavior of the foot. Clinical observations and statistical data indicate that PST degeneration is linked to many common foot and ankle disorders, particularly in elder adults. Such degeneration may lead to pain and contribute to conditions like metatarsalgia, plantar fasciitis, hallux valgus, and complications in diabetic foot conditions [\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile FAM and PSTT are common indicators for evaluating foot health clinically, the evaluation methods exhibit subjectivity and lack standardized criteria. In clinical practice, doctors typically manually annotate and measure radiological imaging results to assess FAM, focusing on parameters such as the calcaneal pitch angle (CPA) and the talo-first metatarsal angle (TMA or Meary's angle) to diagnose conditions like flatfoot and high arches [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, these angles can be measured using approximately four to six different methods [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and there is no standardized approach for defining and measuring arch height. Additionally, these manual measurements are time-consuming for practitioners.\u003c/p\u003e \u003cp\u003eWorse still, detecting variations in PSTT poses greater challenges. PSTT exhibits strong individualized characteristics, influenced by factors such as age, gender, diseases, and lifestyle [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e][\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], making standardized evaluation difficult. With the advancement of ultrasound technology, ultrasound has become the primary method for detecting PSTT [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e][\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e][\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additionally, with the development of computer technology and advanced medical imaging techniques, studies employing visual image processing techniques, including deep learning and artificial intelligence, for detecting PSTT in foot medical imaging have been widely reported [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e][\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e][\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Although some literatures have conducted meta-analyses on deep learning image segmentation for PST, there is still a lack of analysis regarding the correlation between PSTT and factors such as gender, age, and footwear habits, especially based on large data samples for comprehensive statistical parameter information on PSTT. Furthermore, due to the difficulty in collecting medical data, previous studies have not analyzed the correlation between foot skeletal structure and PST based on large data volumes. Therefore, previous investigations have provided inconsistent and inconclusive results regarding the relationship between FAM and PSTT, without sufficient exploration into the impact of demographic factors such as gender and age on this relationship. However, it\u0026rsquo;s a meaningful task to explore the associations between FAM, PSTT, and different population categories, as well as their intrinsic correlations. This work is crucial for accurate and convenient foot health assessment and diagnosis, understanding foot biomechanical structures, disease prevention and rehabilitation, footwear and orthotic design, and foot modeling, etc.\u003c/p\u003e \u003cp\u003eTo address the aforementioned challenges, our study amassed a substantial dataset of weight-bearing lateral foot X-ray images, a type for which there is currently no publicly available dataset. A set of 1497 images is retrospectively collected from the foot and ankle database of Huashan Hospital (Shanghai, China) spanning the last decade, with the personal info anonymized and ethic review approved. Utilizing deep learning image segmentation techniques, we preprocessed these images by adjusting grayscale, removing noise, and normalizing the images, enhancing the model's robustness, stability, and accuracy [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. We then trained a deep neural network to perform precise segmentation of the first metatarsal (FM), talus (TA), calcaneus (CA), navicular (NAVI) bones, as well as the overall foot boundary. This approach enabled automated, standardized, and batch processing for precise computations of FAM and PSTT, thereby yielding significant time and cost efficiencies.\u003c/p\u003e \u003cp\u003eFurthermore, our research interest lies in analyzing the homogeneity and heterogeneity of large data samples, using data-driven approaches to discover patterns of similarity and dissimilarity among population groups. We further investigated the correlation between FAM and PSTT among different population groups. The section 2 describes the methodology, covering the data source, composition, and preprocessing of the dataset, as well as the development of deep learning image segmentation models and the evaluation metrics for FAM and PSTT. Section 3 presents the results, detailing the performance of the image segmentation models, the data results for FAM and PSTT, and the analysis of the correlation between FAM and PSTT across different demographic groups. Section 4 discusses the methodologies, results, and underlying hypotheses, followed by a summary and future perspectives of the study. The overall workflow of the study is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Dataset\u003c/h2\u003e \u003cp\u003eThe application of deep learning for image detection and segmentation requires a substantial dataset. Due to the unavailability of public X-ray image datasets, we undertook a retrospective data collection to facilitate efficient and cost-effective research. We compiled 1497 weight-bearing lateral full foot X-ray images from Huashan Hospital's foot and ankle imaging database, spanning from 2013 to 2022, involving 1098 patients. The data, stored in DICOM file format [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e][\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], includes demographic details such as gender, age, and imaging timestamps. All data samples were anonymized during processing and subsequent research phases to ensure privacy. Additionally, to account for the developmental stage of children\u0026rsquo;s skeletons, we excluded samples from individuals under the age of 14 years. The collection process also involved manual screening by foot and ankle surgeons to exclude images from patients with skeletal or soft tissue foot defects, a history of foot ulcers, neurological joint diseases, post-foot surgery conditions, and those unable to walk independently.\u003c/p\u003e \u003cp\u003eThe X-ray images were sourced from medical imaging devices produced by several manufacturers, including GE, Canon, Philips, CARESTREAM, and KODAK. These devices capture images with an average pixel spacing of approximately 0.14 mm/pixel. The X-rays are collected as grayscale images with a depth of 16 bits, and the average resolution of the collected images is 2507\u0026times;2080 pixels. For visualizing and processing the X-ray images, we employed the PyDicom library, a medical image processing tool, to parse DICOM files and convert the X-ray grayscale images into JPG format for easier handling [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the basic information of these data samples. In this study, each X-ray image is treated as an individual data sample. This includes both left and right foot X-ray images of the same patient and multiple images taken from the same patient over the past decade, without filtering for duplicate individuals in the dataset.\u003c/p\u003e \u003cp\u003eTable 1 Statistical information of data samples\u003c/p\u003e \u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Image preprocessing\u003c/h2\u003e \u003cp\u003eFrom the dataset, 220 images were randomly selected and divided into training, validation, and testing sets, with 180, 20, and 20 images respectively. Under the supervision of foot and ankle surgeons, these images were manually annotated for precise boundary delineation of the entire foot and the four bone structures: FM, TA, CA, and NAVI, using the LabelMe library. These annotations served as the ground truth for model training [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Once the model's accuracy and generalization were confirmed, it was applied to all sampled images to automatically calculate metrics related to FAM and PSTT. This facilitated large-scale data analysis to investigate the factors influencing these measurements.\u003c/p\u003e \u003cp\u003eAdditionally, to enhance the robustness and generalization capability of the model, we employed the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm for contrast enhancement [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and converted the 16-bit X-ray image into 8-bit images with sufficient contrast. Subsequently, image normalization is performed to reduce differences in brightness and contrast, mitigating the model's susceptibility to outliers or extreme pixel values. Next, in order to reduce computational complexity and memory usage, we utilized the bilinear interpolation method [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] for resizing the original images to a unified size of 384\u0026times;576 pixels as input for the model, and ensured that the key semantic information in the images was preserved. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates an example weight-bearing lateral foot X-ray image, where (a) is the original grayscale image parsed from the DICOM file, serving as the input to the model, (b) displays the manual labeling results using the Labelme library, stored as a JSON file, while (c-g) illustrate the boundaries of the entire foot, FM, TA, CA and NAVI bones obtained from parsing the JSON file. Here, for visual clarity, these label boundaries are overlaid on the original image for visualization, though each labeled image is actually a binary black-and-white image. And (h) shows the visualization of various labeled images with different pixel values overlaid on one image.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Deep learning image segmentation model\u003c/h2\u003e \u003cp\u003eIn our selection of deep learning network models for the task of calcaneus (CA) segmentation, we evaluated four widely used models in medical image segmentation: FCN [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], U-Net [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], SegNet [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and DeepLab V3+ [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Among these, DeepLab V3\u0026thinsp;+\u0026thinsp;demonstrated superior performance, particularly in detailed boundary segmentation and generalization capabilities. Consequently, we chose the DeepLab V3\u0026thinsp;+\u0026thinsp;model for automatic image segmentation. To enhance robustness and accuracy, we constructed five independent DeepLab V3\u0026thinsp;+\u0026thinsp;segmentation models, one for the entire foot boundary and one for each of the four bone boundaries (FM, TA, CA, NAVI). Each model was trained separately to optimize parameters. The input image dimensions were standardized to 384x576 pixels, and outputs were binarized using the sigmoid function [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. To ensure reproducibility, all training runs were conducted with fixed seed settings. PyTorch was used for model construction and training, with parameters as follows: Adam optimizer [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], learning rate is set to 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, batch size is set to 4, epoch is set to 20. The environment and versions are macOS Ventura 13.2.1, 4-cores CPU, 16GB RAM, PyTorch version 1.8. For the loss function and evaluation metrics, we selected the Dice coefficient and Intersection over Union (IoU). The Dice coefficient is particularly sensitive to small targets, making it ideal for precise segmentation of smaller anatomical structures, while IoU is well-suited for large target detection and segmentation tasks. Therefore, we utilized Dice loss for training to optimize our model's ability to detect small variations, and employed IoU as the evaluation metric to assess the overall accuracy and integrity of the segmentation across larger areas [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, in the test set, in rare extreme cases where X-ray images contained high-intensity artifacts, the model might misclassify noise and contamination during segmentation. Therefore, post-processing was applied to the segmentation masks using the DBSCAN algorithm for clustering [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This step retained the largest clustered area as the target region and set the values of smaller misclassified noise regions to 0, eliminating interference in subsequent tasks such as extracting bone axes and calculating PSTT.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Calculation and evaluation of FAM and PSTT indicators\u003c/h2\u003e \u003cp\u003eIn this study, we focused on three primary descriptors of FAM as advised by foot and ankle surgeons: the angle between the axes of the first metatarsal and the talus (\u0026ldquo;angle-fm-ta\u0026rdquo;), the inclination of the calcaneus axis relative to the plantar surface (\u0026ldquo;angle-ca-plantar\u0026rdquo;), and the longitudinal arch height (LAH). Additionally, we measured PSTT at the forefoot and rearfoot regions.\u003c/p\u003e \u003cp\u003eTo calculate the \u0026ldquo;angle-fm-ta\u0026rdquo; and \u0026ldquo;angle-ca-plantar\u0026rdquo; in weight-bearing lateral foot X-ray images, we first applied the Principal Component Analysis (PCA) algorithm [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] to determine the principal axes of the segmented FM, TA, and CA bones. We then calculated the angle between the principal axes of the FM and TA to determine the \u0026ldquo;angle-fm-ta.\u0026rdquo; This method mirrors the standardized manual angle measurements performed by surgeons using X-ray reading software, reducing subjective variability. The \u0026ldquo;angle-ca-plantar\u0026rdquo; was defined as the angle between the main axis of the CA and the horizontal plane, as suggested by surgeons.\u003c/p\u003e \u003cp\u003eFor the calculation of LAH, we identified the center of the NAVI bone based on the PCA algorithm and defined it as the distance from the NAVI bone center to the median of the PST boundary points on the forefoot and rearfoot. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays schematic diagrams of these measurements for both the left (a) and right feet (b). Notably, here we stipulated that the \u0026ldquo;angle-fm-ta\u0026rdquo; is the angle between the FM axis and the TA axis, potentially resulting in angles greater than 180\u0026deg;.\u003c/p\u003e \u003cp\u003eWe also measured PSTT by calculating the distance from the lowest boundary point of the FM to the foot's lower border directly beneath it, denoted as the forefoot PSTT (arrow A in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Similarly, the rearfoot PSTT was measured from the lowest point of the CA to the foot boundary beneath it (arrow B in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor comparative analysis, we calculated the foot length (FL), defined as the distance between the outermost points of the toe and heel, marked by a red line in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The LAH and PSTT values were then normalized by dividing by the FL, resulting in normalized indicators: normalized LAH, normalized forefoot PSTT, and normalized rearfoot PSTT.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Image segmentation model results\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;2 summarizes the performance of five segmentation model on the training, validation, and test set respectively. It is observed that among the five segmentation tasks, the performance of the entire foot segmentation model is the best. Besides, all models demonstrate good performance and generalization, thereby avoiding the issue of overfitting.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;2. Accuracy of DeepLab V3\u0026thinsp;+\u0026thinsp;Image Segmentation Models\u003c/p\u003e \u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e showcases the automated segmentation results of a randomly selected image from the testing set. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(b-f) illustrate the segmentation outcomes for the entire foot boundary, and the FM, TA, CA, and NAVI bone regions, respectively. These results highlight precise delineation of the foot outline and the CA boundaries, demonstrating the model's effectiveness in these areas. However, there are minor discrepancies in the segmentation of the FM, TA, and NAVI bones, primarily due to the challenges inherent in automatically segmenting these complex structures. Similar to the manual annotation process, which even experienced foot and ankle surgeons find time-consuming and demanding in terms of precise boundary positioning, the segmentation of these smaller and more intricate boundaries presents considerable challenges for the models. Despite these challenges, the performance of the models is relatively satisfactory. In the test set, foot and ankle surgeons manually reviewed and validated the segmentation results, noting a high overlap rate with the actual bone boundaries. These minor discrepancies were deemed to have negligible impact on subsequent computations and analyses of FAM and PSTT, confirming the model's utility and accuracy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e demonstrates the comparative effectiveness of using the DBSCAN method for handling outlier data with artifacts. It is visually apparent that this method effectively rectifies errors in the DeepLabV3\u0026thinsp;+\u0026thinsp;model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Result of FAM analysis\u003c/h2\u003e \u003cp\u003e \u003cem\u003eA. Distribution of FAM characteristic indicators\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e (a), (b), and (c) show the statistical histogram of the angle-fm-ta, angle-ca-plantar, and the normalized LAH respectively, obtained through automated calculations via image segmentation and PCA. It is evident from the figures that the distribution of the angle-fm-ta is mainly concentrated around 180\u0026deg; (182.151\u0026deg;\u0026thinsp;\u0026plusmn;\u0026thinsp;11.433\u0026deg;), which is consistent with the normal FAM in medicine. The angle-ca-plantar is mainly distributed around 12\u0026deg; (11.941\u0026deg;\u0026thinsp;\u0026plusmn;\u0026thinsp;6.169\u0026deg;), but a small number of negative values are observed, which were subsequently diagnosed by orthopedic surgeons as cases of flatfoot during further analysis. The mean and standard deviation of the normalized LAH are calculated as 0.214 times FL and 0.034 times FL, respectively. Analysis of the coefficient of variation (angle-fm-ta CV\u0026thinsp;=\u0026thinsp;0.063, angle-ca-plantar CV\u0026thinsp;=\u0026thinsp;0.517, normalized LAH CV\u0026thinsp;=\u0026thinsp;0.160) for the three metrics indicates that the angle-fm-ta exhibits the highest stability across various samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eB. Interrelationships among FAM characteristic indicators\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, there is a strong correlation among the three characteristic indicators of FAM. The normalized LAH exhibits a negative correlation with the angle-fm-ta, while showing a positive correlation with the angle-ca-plantar. Subjects with larger angle-fm-ta tend to have smaller angle-ca-plantar, and may even become negative in some cases. Additionally, their normalized LAH tends to be lower. Such characteristics are associated with flatfoot conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eC. Relationship between FAM indicators and gender\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e illustrates the variations in the distributions of angle-fm-ta, angle-ca-plantar, and normalized LAH, highlighting the influence of gender on these metrics. The data shows that the angle-fm-ta is generally higher in females than in males. Additionally, there is a strong correlation among these three indicators; correspondingly, the angle-ca-plantar and normalized LAH are both observed to be smaller in females compared to males. This pattern underscores the impact of gender-specific anatomical differences on these foot arch morphology metrics.\u003c/p\u003e \u003cp\u003e \u003cem\u003eD. Relationship between FAM and age\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWe categorized the age distribution of all collected samples into eight groups, ranging from 14 to 90 years old, with the following brackets: [\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18 CR19\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], [\u003cspan additionalcitationids=\"CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], [41\u0026ndash;50], [51\u0026ndash;60], [61\u0026ndash;70], [71\u0026ndash;80], and [81\u0026ndash;90]. This organization was made after excluding samples from individuals under the age of 14. Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003e(a-c) displays the box plots for the distribution of three evaluation indicators of (FAM) across these age groups. The plots indicate that there is no significant correlation between FAM and age, as the data distribution shows minimal variation among the different age categories.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Result of PSTT analysis\u003c/h2\u003e \u003cp\u003e \u003cem\u003eA. Relationship between PSTT and gender\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;3 records the distribution of PSTT for all data samples, categorized by gender. The mean PSTT reported in the table represents the average thickness at both the forefoot and rearfoot. The data reveal a notable difference in PSTT between these two regions, with an average discrepancy of approximately 0.5mm. This measurement was specifically chosen to compare PSTT below the FM and below the CA. The observed variability between these areas provides a foundation for further investigation into regional differences in PSTT across the foot. The distribution of forefoot and rearfoot PSTT normalized by foot length among different genders is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e10\u003c/span\u003e. The results indicate that the PSTT of males is thicker than that of females, especially with a more pronounced difference in the forefoot PSTT.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;3. The average result of plantar soft tissue thickness (with grouping by gender)\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"530\" height=\"146\"\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eB. Relationship between PSTT indicators and age\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;4 summarizes the results of PSTT grouped by age. Due to the limitation of retrospective data collection, it is not feasible to subjectively intervene in achieving a balanced distribution of sample sizes within each age group.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;4. The average result of plantar soft tissue thickness (with grouping by age)\u003c/p\u003e \u003cp\u003e\u003cimg 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\" width=\"566\" height=\"176\"\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e11\u003c/span\u003e(a) and (b) provide a clearer and more intuitive depiction of how normalized forefoot and rearfoot PSTT varies with age within each group. From these figures, it is evident that PSTT at both the forefoot and rearfoot follows a similar trend with age, initially increasing and then decreasing as age progresses. This trend can be partly attributed to the fact that adolescents in the [\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18 CR19\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] age group are still undergoing developmental changes, which may influence measurements due to factors like skeletal growth, height, and changes in foot length. Additionally, the sample size for the [81\u0026ndash;90] age group is only 18, significantly smaller than other age groups, making the data susceptible to outliers and potentially distorting the overall results. After excluding data from the [\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18 CR19\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and [81\u0026ndash;90] age groups, the trend of decreasing PSTT in older age groups becomes more pronounced, indicating that the elderly tend to have thinner plantar soft tissues compared to those in the middle-aged group, with more noticeable changes particularly in the heel area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, when examining the impact of age on PSTT, we also considered the influence of gender. Figure\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e12\u003c/span\u003e displays the data distribution and performance results of mean PSTT for males and females within each age group, aligning with the gender influence trends discussed earlier. Across most age groups, except for the [81\u0026ndash;90] group, the PSTT for males is generally greater than that for females. Furthermore, the trend across age groups for both genders shows an initial increase in PSTT, followed by a decrease as age advances. This pattern highlights the nuanced interplay between age and gender in influencing PSTT.\u003c/p\u003e \u003cp\u003e \u003cem\u003eC. Relationship between PSTT and era\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe longitudinal trends and cross-sectional differences in PSTT over the past decade with gender and age has also been investigated. Despite the uneven distribution of sample numbers across different years, it is still feasible to conduct longitudinal comparative analyses to explore the impact of generational changes on PSTT, while holding gender and age as constant influencing factors. Figure\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e13\u003c/span\u003e illustrates the temporal distribution of PSTT for males and females. Notably, there is a discernible decreasing trend in forefoot PSTT among females in recent years, comparing to the slight increase in male. It may hypothesize that this trend may be attributed to the fashion change in shoe wearing habit, such as high-heel, among modern women compared to earlier generations, however further investigations and evidences are necessary to approach the conclusion. Figure\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e14\u003c/span\u003e illustrates the mean value of PSTT within age groups from 2013 to 2022, with (a) and (b) representing the data performance of forefoot and rearfoot PSTT, respectively. In years with fewer data samples, certain age groups lack data support, for instance, only three age groups have data for 2014.\u003c/p\u003e\u003cp\u003eDespite excluding the sparse data from 2014, our current analysis has not revealed significant impacts of era differences on PSTT. This lack of clear trends may stem from the challenges associated with accounting for variations in potential influencing factors both within and between groups, such as weight, BMI, footwear habits, and activity levels, given the limited scale of the available data. Consequently, future research should focus on gathering a larger dataset or designing studies with more stringent controls over these variables to facilitate a more in-depth exploration of the influences on PSTT..\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Correlation between FAM and PSTT\u003c/h2\u003e \u003cp\u003eThe foot arch and PST both serve critical functions in supporting body weight, bearing pressure, and cushioning impact forces. Their interactions influence each other, leading to changes in FAM and PSTT. Based on clinical advice, we examined the association between LAH and PSTT, the correlation between the angle-fm-ta and forefoot PST degeneration, and the relationship between the angle-ca-plantar and rearfoot PST.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e15\u003c/span\u003e presents the data distribution and a linear regression analysis examining the relationship between LAH and PSTT across different genders. The PSTT is considered as an overall mean value of both forefoot and rearfoot, with a brown line representing the linear regression fit for the entire data sample, irrespective of gender. It indicates that individuals with lower arches, particularly those with flat feet, tend to experience increased load on the PST, making the soft tissues more susceptible to degeneration and consequently resulting in relatively thinner PST.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding the study relationship between the angle-fm-ta and forefoot PST degeneration, the correlation grouped by genders are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e16\u003c/span\u003e. The bold blue line represents the overall data correlation. The overall trends for either males or females show a negative correlation. For samples with larger angle-fm-ta (greater than 180\u0026deg;), indicating with a flatter foot arch, there is a greater impact on the degeneration of the forefoot PST, resulting in a smaller forefoot PSTT.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFollowing this, we explored the relationship between theangle-ca-plantar and rearfoot PSTT. Our analysis revealed that the impact of the angle-ca-plantar on the degeneration of rearfoot soft tissues is significantly correlated with the variation in thickness between the forefoot and rearfoot soft tissues. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e17\u003c/span\u003e, the term \u0026ldquo;ca-fm PSTT diff\u0026rdquo; refers to the normalized rearfoot PSTT minus the normalized forefoot PSTT. The figure demonstrates a negative correlation between the angle-ca-plantar and this thickness differential, with the data for females showing greater variability in these normalized thickness differences. A larger angle-ca-plantar suggests a heightened burden on the rearfoot soft tissues, which accelerates their degeneration relative to the forefoot. This results in a smaller disparity in thickness between the forefoot and rearfoot soft tissues.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilarly, we conducted statistical analysis on the age groups of data samples, considering the influence of gender on the above three types of correlation within different age groups. The results are illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig21\" class=\"InternalRef\"\u003e18\u003c/span\u003e, \u003cspan refid=\"Fig22\" class=\"InternalRef\"\u003e19\u003c/span\u003e, and \u003cspan refid=\"Fig23\" class=\"InternalRef\"\u003e20\u003c/span\u003e, respectively. It can be observed that, apart from the relatively small number of male samples in the [71, 80] age group and the extremely small sample size in the [81, 90] group, the correlation trends in other groups are generally consistent with the overall data correlation trends analyzed above, with minimal inter-group differences in performance. Additionally, in the age group [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], there are more outliers that deviate from the fitted curve. We attribute this phenomenon mainly to the fact that adolescents are still in their growth phase, and their bones are relatively weak in terms of accumulating long-term stress and undergoing structural changes, thus the data may be influenced by factors such as skeletal development, height, and foot length changes.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, the absence of publicly available datasets necessitated manual annotation to construct a dataset for training deep learning models. Manual annotation of medical X-ray images, particularly for foot and ankle structures, involves significant cost and effort. For foot and ankle surgeons, accurately delineating the boundaries of the FM, TA, CA, and NAVI bones in a single lateral foot X-ray image is a labor-intensive and time-consuming task. A total of 220 images were randomly selected from all available samples for use in the segmentation models. This number was chosen based on the high cost associated with data annotation and the satisfactory model performance observed using 180 images for training.\u003c/p\u003e \u003cp\u003eAdditionally, although our dataset was constructed through random sampling, a small proportion of the images containing noise artifacts is inevitably not included in the model training, which could potentially affect the model's output accuracy, necessitating the use of post-processing methods like DBSCAN to correct errors. Furthermore, five DeepLab V3\u0026thinsp;+\u0026thinsp;segmentation models were developed, each dedicated to segmenting the entire foot boundary and the four specific bone regions (FM, TA, CA, NAVI). The performance of each model varied, largely due to the distinct complexity associated with each segmentation task. Segmenting the entire foot boundary was relatively straightforward due to its prominent and distinct features. However, segmenting the boundaries of individual bones posed greater challenges because of their interconnected and overlapping feature in lateral X-ray images. Despite these complexities, the models generally produced satisfactory results.\u003c/p\u003e \u003cp\u003eMoreover, manual annotation of bone boundaries in lateral foot X-rays presents significant challenges due to the three-dimensional skeletal structures being projected onto a two-dimensional plane, resulting in overlaps. Particularly challenging is the annotation of the talus, where both the medial and lateral surfaces of the upper part of the talus neck are visible and overlap due to varus and valgus deformities. The selection of which part to define as the boundary of the talus significantly impacts the calculation of its main axis. After extensive experimentation with various labeling methods, foot and ankle surgeons determined that the most accurate approach is to select the lower boundary of the projection of the talar roof as the upper edge, and the talocalcaneal articular surface as the lower edge, ignoring any lateral protrusions. This method ensures that the main axis results extracted by PCA align more closely with the actual anatomical structure.\u003c/p\u003e \u003cp\u003eAnother aspect that merits discussion involves the evaluation indicators for FAM and PSTT. Clinically, the talus-first metatarsal angle (also known as Meary's angle) in a lateral standing position typically sees the inferior talus oblique line extending through and being collinear with the first metatarsal axis, forming an angle of 0\u0026deg;, or slightly lower than the first metatarsal axis. The definition of when the talus-first metatarsal angle is positive or negative remains controversial, however in this work, the angle is specified as 180\u0026deg; for the collinear case.\u003c/p\u003e \u003cp\u003eFurthermore, clinical evaluations of FAM often focus more on the inclination of the calcaneus along its superior oblique line rather than the inclination angle of the calcaneus axis (angle-ca-plantar). For clarity and simplicity in our analysis, and to better understand the trends and correlations with various factors, we opted to use the calcaneal axis inclination angle as our evaluation index. The superior calcaneal oblique line is typically drawn between two points: the first along the inferior surface of the calcaneocuboid joint and the second along the anteroinferior aspect of the medial tubercle.\u003c/p\u003e \u003cp\u003eRegarding the PSTT, the forefoot primarily bears the support and balance function, while the rearfoot mainly provides support and propulsion to maintain body balance and stability [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Therefore, the load-bearing and degenerative conditions of PST differ across different locations. In lateral foot X-rays, we artificially defined the PSTT for the forefoot and rearfoot. Further detailed studies on the similarities and differences of PST at different locations may require a more nuanced distinction of PST regions.\u003c/p\u003e \u003cp\u003eLastly, another limitation of this study stems from the challenges associated with collecting medical imaging data, which is inherently time-consuming, costly, and involves sensitive information. Our retrospective approach to data collection introduced difficulties in controlling for variables such as gender, age, and era distributions, and limited our ability to obtain sensitive physiological and clinical data beyond basic demographics. Critical variables such as weight, BMI, footwear habits, activity levels, and medical history, which might reveal intergroup differences, were not available in the current dataset.\u003c/p\u003e \u003cp\u003eDue to these constraints in data collection, certain subgroup analyses, particularly those investigating the impacts of age and era, may suffer from insufficient sample sizes to yield conclusive results. While we analyzed the potential influence of different eras on each investigated correlation, the uneven distribution of samples and the possibility that the existing volume of data may still be inadequate meant that not all analyses could be included in the \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003eresults\u003c/span\u003e section. Consequently, we cannot definitively claim that era has no impact on FAM and PSTT.\u003c/p\u003e \u003cp\u003eLooking ahead, analyzing a larger and more diverse dataset could lead to more comprehensive and robust conclusions. Additionally, for some of the relationships currently identified, such as the influence of gender on plantar soft tissue thickness, it remains challenging to determine whether differences between male and female groups in other variables might affect these outcomes. Thus, designing further single-variable experiments is necessary to validate our current hypotheses and provide a clearer understanding of these relationships.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study retrospectively collected weight-bearing lateral foot X-ray image data over the past decade, and devised a method based on deep learning image segmentation model to automatically extract FAM and PSTT from X-ray images accurately. By manually annotating and constructing the training dataset, DeepLabV3\u0026thinsp;+\u0026thinsp;network model was trained to achieve precise segmentation of the FM, TA, CA and NAVI bone structures, and the entire foot boundary region. The PCA method was employed to extract the principal axes of bones to obtain the angle evaluation indicators for FAM. Compared to traditional manual measurements, this method not only enhances detection efficiency but also ensures accuracy and objectivity. It provides a consistent and reliable standardized quantitative method, laying a foundation for establishing big data correlation analysis of FAM and PSTT. Furthermore, based on the data samples and considering population differences and various features, correlation analyses and studies were conducted. Changes and variations in FAM and PSTT influenced by gender, age, and era were analyzed. Next, attempts were made to explore the correlation analysis between FAM and PSTT, as well as discussions based on gender and age differences. The overall trend of correlation within different gender and age groups was the same, and there was no significant difference.\u003c/p\u003e \u003cp\u003eAdmittedly, the dataset used in this study was retrospectively collected, making it difficult to control the balance of data quantities within gender, age, and era groups. Furthermore, it was not possible to obtain more clinical information and sensitive personal information, making it impossible to fully eliminate differences in other influencing factors within each group. Therefore, the hope lies in the collection of more sample data in future research and the design of more rigorous single-variable experiments to reduce inter-group interference. Subsequent analysis of more samples in future studies holds promise for arriving at more comprehensive and robust conclusions. However, the method introduced in this work, which explores the correlation and influencing factors of FAM and PSTT using deep learning and data-driven approaches, along with the current research results, provides a potential valuable foundation for further theoretical and practical explorations, and can serve as an automated method for evaluating flatfeet or high arched feet. The correlation and analysis studies between FAM, PSTT, and different influencing factors conducted in conjunction with population differences offer new perspectives on related studying. This work establishes a robust tool for clinical research and offers significant guidance for developing personalized interventions for foot-related diseases. Additionally, it has implications for optimizing footwear design to enhance foot health and performance, making it a valuable resource for both medical and industry applications.\u003c/p\u003e "},{"header":"Declarations","content":" \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003ch2\u003eAuthor Contributions Statement\u003c/h2\u003e \u003cp\u003eX.M. supervised and coordinated project; R.H., L.C. and J.Z. designed research; L. C. directed clinical trials, X.N. and T.R. performed research; X.N. analyzed data; and R.H. and X.N. wrote paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eX.M. supervised and coordinated project; R.H., L.C. and J.Z. designed research; L. C. directed clinical trials, X.N. and T.R. performed research; X.N. analyzed data; and R.H. and X.N. wrote paper.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eThis work is financially supported by the National Key Research and Development Program China (2022YFC2009500), the Medical Engineering Fund of Fudan University (YG2021-005, YG2022-008), the Fudan-Yiwu Fund (FYX-23-102), and the TZI-ZJU Industrial Program (2023CLG01, 2023CLG01PT).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003e\u0026bull; The main data generated or analyzed to support the conclusion during this study are included in this published article and its supplementary information files.\u0026bull; The full datasets generated and/or analyzed during the current study are not publicly available due PRIVACY PROTECTION POLICY AND ETHIC REQUIERMENT but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKeller, T. 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Contributions of foot muscles and plantar fascia morphology to foot posture. Gait \u0026amp; posture, 61, 238-242.\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Image segmentation, big data analysis, foot arch morphology, plantar soft tissue, X-ray images","lastPublishedDoi":"10.21203/rs.3.rs-4409140/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4409140/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe morphological characteristics of the foot arch and the plantar soft tissue thickness are pivotal in assessing foot health, which is associated with various foot and ankle pathologies. By applying deep learning image segmentation techniques to lateral weight-bearing X-ray images, this study investigates the correlation between foot arch morphology (FAM) and plantar soft tissue thickness (PSTT), examining influences such as age, gender, health status, physical activity, and footwear habits. Specifically, we use the DeepLab V3\u0026thinsp;+\u0026thinsp;network model to accurately delineate the boundaries of the first metatarsal, talus, calcaneus, navicular bones, and overall foot, enabling rapid and automated measurements of FAM and PSTT. A retrospective dataset containing 1,497 X-ray images is analyzed to explore associations between FAM, PSTT, and various demographic factors. Our findings contribute novel insights into foot morphology, offering robust tools for clinical assessments and interventions. The enhanced detection and diagnostic capabilities provided by precise data support facilitate population-based studies and the leveraging of big data in clinical settings.\u003c/p\u003e","manuscriptTitle":"Deep Learning-Assisted Segmentation of X-ray Images for Rapid and Accurate Assessment of Foot Arch Morphology and Plantar Soft Tissue Thickness","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-30 14:35:33","doi":"10.21203/rs.3.rs-4409140/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2024-06-14T02:54:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-29T07:52:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"152187340156914098396480131411438362234","date":"2024-05-21T14:17:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312750567654172248473775454432621437625","date":"2024-05-20T09:41:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-20T08:46:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-20T08:31:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-14T12:32:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-14T12:27:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-05-12T16:08:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9432abe5-0ad4-4898-982d-78916dcba755","owner":[],"postedDate":"May 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":32210871,"name":"Biological sciences/Computational biology and bioinformatics/Image processing"},{"id":32210872,"name":"Biological sciences/Computational biology and bioinformatics/Machine learning"},{"id":32210873,"name":"Physical sciences/Physics/Biological physics"}],"tags":[],"updatedAt":"2024-09-02T16:02:22+00:00","versionOfRecord":{"articleIdentity":"rs-4409140","link":"https://doi.org/10.1038/s41598-024-71025-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-08-28 15:57:39","publishedOnDateReadable":"August 28th, 2024"},"versionCreatedAt":"2024-05-30 14:35:33","video":"","vorDoi":"10.1038/s41598-024-71025-x","vorDoiUrl":"https://doi.org/10.1038/s41598-024-71025-x","workflowStages":[]},"version":"v1","identity":"rs-4409140","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4409140","identity":"rs-4409140","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}