Semantic Segmentation and Object Detection for 3D Motion Analysis of the Ankle Joint in High-Resolution MRI

Although high-resolution MRI provides excellent anatomical detail, existing segmentation approaches possess a requisite yet inadequate level of precision, require substantial human effort, and fail to accurately represent the intricate 3D structure. To address these limitations, this work develops a novel 3D Faster R-CNN engine that automatically detects and segments the main ankle joint components from volumetric MRI. The proposed design combines a 3D ResNet-50 transformer with a 3D Region Proposal Network and 3D ROI Align components to analyze MRI scans. The model trained with experiments based on ankle MRI datasets from second-party repositories used data processing steps to normalize image size and enhance dataset collection. The assessment metrics consisted of Dice Similarity Coefficient, Intersection over Union, and mean Average Precision (mAP). By evaluating several models, the system achieves a Dice coefficient score of 91.4% alongside an mAP of 89.6% at IoU 0.5 which beats previous 2D and 3D segmentation techniques. Scientific images showed that the method could precisely detect body structures in different MRI views while keeping their correct shapes.