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

TL;DR

This paper presents an automated cartilage segmentation method using a 3D Swin UNETR to facilitate subject-specific knee joint finite element modeling, achieving high accuracy and comparable biomechanical predictions to manual segmentation.

Contribution

The study introduces an automated segmentation pipeline with geometry filtering for knee cartilage, improving efficiency while maintaining accuracy for biomechanical modeling.

Findings

Automated segmentation achieved high Dice scores (89.4% femoral, 85.1% tibial).

Hausdorff distance between automated and manual segmentation was 2.3 mm.

Biomechanical simulation results showed no significant differences between automated and manual models.

Abstract

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.