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Abstract #2666

Automated Synovitis Segmentation in Patients with Rheumatoid Arthritis

Sibaji Gaj1, Ceylan Colak2, Mingrui Yang1, Kunio Nakamura1, and Xiaojuan Li1
1Department of Biomedical Engineering, Cleveland Clinic, Cleveland, OH, United States, 2Department of Radiology, Cleveland Clinic, Cleveland, OH, United States

Synovitis is a very common finding in joints of RA patients, which may serve as biomarkers for early diagnosis and for early treatment response evaluation. However, synovitis quantification is challenging because manual segmentation of such irregular lesions is tedious and prone to inter reader variation. In this work, we implemented a fully automatic segmentation algorithm for synovitis lesions in wrist Magnetic Resonance images in subjects with RA using deep learning based conditional generative adversarial networks and U-Net. Using a small number of training data, the proposed model demonstrated feasibility of fully automatically synovitis segmentation with reasonable accuracy (Dice coefficient 0.78).

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