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Simulation studies like finite element (FE) modeling provide insight intoknee joint mechanics without patient experimentation. Generic FE modelsrepresent biomechanical behavior of the tissue by overlooking variations ingeometry, loading, and material properties of a population. On the other hand,subject-specific models include these specifics, resulting in enhancedpredictive precision. However, creating such models is laborious andtime-intensive. The present study aimed to enhance subject-specific knee jointFE modeling by incorporating a semi-automated segmentation algorithm. Thissegmentation was a 3D Swin UNETR for an initial segmentation of the femur andtibia, followed by a statistical shape model (SSM) adjustment to improvesurface roughness and continuity. Five hundred and seven magnetic resonanceimages (MRIs) from the Osteoarthritis Initiative (OAI) database were used tobuild and validate the segmentation model. A semi-automated FE model wasdeveloped using this semi-automated segmentation. On the other hand, a manualFE model was developed through manual segmentation (i.e., the gold standardapproach). Both FE models were subjected to gait loading. The predictedmechanical response of manual and semi-automated FE models were compared. Inthe result, our semi-automated segmentation achieved Dice similaritycoefficient (DSC) over 98% for both femur and tibia. The mechanical results(max principal stress, max principal strain, fluid pressure, fibril strain, andcontact area) showed no significant differences between the manual andsemi-automated FE models, indicating the effectiveness of the proposedsemi-automated segmentation in creating accurate knee joint FE models. (https://data.mendeley.com/datasets/k5hdc9cz7w/1 ).

Integration of Swin UNETR and statistical shape modeling for a  semi-automated segmentation of the knee and biomechanical modeling of  articular cartilage