  Simulation studies, such as finite element (FE) modeling, offer insights into
knee joint biomechanics, which may not be achieved through experimental methods
without direct involvement of patients. While generic FE models have been used
to predict tissue biomechanics, they overlook variations in population-specific
geometry, loading, and material properties. In contrast, subject-specific
models account for these factors, delivering enhanced predictive precision but
requiring significant effort and time for development. This study aimed to
facilitate subject-specific knee joint FE modeling by integrating an automated
cartilage segmentation algorithm using a 3D Swin UNETR. This algorithm provided
initial segmentation of knee cartilage, followed by automated geometry
filtering to refine surface roughness and continuity. In addition to the
standard metrics of image segmentation performance, such as Dice similarity
coefficient (DSC) and Hausdorff distance, the method's effectiveness was also
assessed in FE simulation. Nine pairs of knee cartilage FE models, using manual
and automated segmentation methods, were developed to compare the predicted
stress and strain responses during gait. The automated segmentation achieved
high Dice similarity coefficients of 89.4% for femoral and 85.1% for tibial
cartilage, with a Hausdorff distance of 2.3 mm between the automated and manual
segmentation. Mechanical results including maximum principal stress and strain,
fluid pressure, fibril strain, and contact area showed no significant
differences between the manual and automated FE models. These findings
demonstrate the effectiveness of the proposed automated segmentation method in
creating accurate knee joint FE models.
