Integration of Swin UNETR and statistical shape modeling for a semi-automated segmentation of the knee and biomechanical modeling of articular cartilage
Description
Our study aimed to enhance subject-specific knee joint FE modeling by incorporating a semi-automated segmentation algorithm. This segmentation was a 3D Swin UNETR for an initial segmentation of the femur and tibia, followed by a statistical shape model (SSM) adjustment 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 the femur, tibia and 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 and SSM models and 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
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Funding
Natural Sciences and Engineering Research Council of Canada
401610