  Simulation studies like finite element (FE) modeling provide insight into
knee joint mechanics without patient experimentation. Generic FE models
represent biomechanical behavior of the tissue by overlooking variations in
geometry, loading, and material properties of a population. On the other hand,
subject-specific models include these specifics, resulting in enhanced
predictive precision. However, creating such models is laborious and
time-intensive. The present 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. A semi-automated FE model was
developed using this semi-automated segmentation. On the other hand, a manual
FE model was developed through manual segmentation (i.e., the gold standard
approach). Both FE models were subjected to gait loading. The predicted
mechanical response of manual and semi-automated FE models were compared. In
the result, our semi-automated segmentation achieved Dice similarity
coefficient (DSC) over 98% for both femur and tibia. The mechanical results
(max principal stress, max principal strain, fluid pressure, fibril strain, and
contact area) showed no significant differences between the manual and
semi-automated FE models, indicating the effectiveness of the proposed
semi-automated segmentation in creating accurate knee joint FE models. (
https://data.mendeley.com/datasets/k5hdc9cz7w/1 ).
