ArticleScientific reports2024
Deep learning-assisted segmentation of X-ray images for rapid and accurate assessment of foot arch morphology and plantar soft tissue thickness.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Subject-Specific Finite Element Analysis of the Foot Using a Simplified Parametric Bone Model from Weight-Bearing Radiographs.Annals of biomedical engineering · 2026Article
- Deep Learning-Based Anatomical Segmentation of the Foot and Ankle: A Multi-View Radiograph Approach.Yonsei medical journal · 2026Article
- Deep Learning-Based Segmentation in Musculoskeletal Imaging: A Review of Research Trends.Journal of the Korean Society of Radiology · 2025Review
- Surgical site soft tissue thickness as a predictor of complications following arthroplasty.World journal of methodology · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
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 of age and sex. 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 1497 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.
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