Deep Learning Based on Knee MRI for Fully Automated Segmentation and Precision Diagnosis of Rheumatoid Arthritis, Gout and PVNS

Segmentation of synovial-related structures in MRI images can help assess synovitis-effusion, infrapatellar fat pad (IPFP) changes, and response to treatment, which is important for the clinical diagnosis of knee disease. However, segmenting images manually, which depends on the skill and experience of the physician; furthermore, it is time-consuming for radiologists. In this study, a deep learning pipeline for the 3D segmentation of the suprapatellar capsule (SC) and IPFP and knee synovitis classification were developed using proton density (PD)-weighted images of sagittal fat-suppressed knees, the most commonly used sequence in clinical practice, to support clinical decision-making.