Deep learning-assisted segmentation of X-ray images for rapid and accurate assessment of foot arch morphology and plantar soft tissue thickness

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.