Fully automated pipeline for localization and scoring of rheumatic disease related regions and pathologies in hand MRI

Rheumatic diseases constitute a major cause of chronic pain, functional impairment, and long-term disability worldwide. In rheumatic diseases, reliable assessment of structural damage and inflammatory activity is essential for diagnosis, disease monitoring, and treatment response evaluation, yet remains largely dependent on time-intensive expert image interpretation. In this study, we develop and evaluate a pipeline for automatic landmark detection and subsequent pathology scoring in hand magnetic resonance images for three main pathologies in rheumatic diseases, namely erosions, osteitis, and synovitis. We explicitly exploit two orthogonal acquisitions (coronal and transversal) by integrating multi-view information at different stages of the pipeline to assess their impact on automatic scoring performance. We train and compare two landmark detection models that utilize all three magnetic resonance imaging sequences to predict predefined landmarks annotated by experts. The YOLO model achieves better landmark predictions for both metrics and across all distances, with a successful detection rate of 94% for a clinically relevant distance of 6 mm and an overall mean Euclidean distance of 3 mm from ground truth landmarks to predicted landmarks. By using a super-resolution approach to fuse coronal and transversal images for the automatic scoring, we achieve an improved performance for synovitis detection. This paves the way for more fully automated precision medicine in magnetic resonance imaging, reducing the workload for physicians while enabling faster, more standardized results to support the decision-making process.