Swin UNETR segmentation with automated geometry filtering for biomechanical modeling of knee joint cartilage

Published: 26 March 2024| Version 1 | DOI: 10.17632/dc832g7j5m.1
Reza Kakavand, Peyman Tahghighi, Reza Ahmadi, W. Brent Edwards, Amin Komeili


Our study aimed to enhance subject-specific knee joint FE modeling by incorporating an automated knee cartilage segmentation algorithm. This segmentation was a 3D Swin UNETR for an initial segmentation of the femoral and tibial cartilages, followed by an automated filtering to improve surface roughness and continuity. Five hundred and seven magnetic resonance images (MRIs) from the Osteoarthritis Initiative (OAI) database were used to build and validate the segmentation model. The masks for cartilages were performed by skilled users from the Zuse Institute Berlin "F. Ambellan, A. Tack, M. Ehlke, and S. Zachow, “Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative,” Med Image Anal, vol. 52, pp. 109–118, 2019." The Swin UNETR models and codes, and the filtering codes have been made publicly available, so researchers can use these models or customize the code for a different dataset to meet their needs. Please refer to our GitHub for any update https://github.com/Rezakaka/knee-segmentation.git



Shape Modeling, Automated Computational Design, Deep Learning