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Abstract #2270

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

Qizheng Wang1, Meiyi Yao2, Yandong Liu3, Xinhang Song2, Xiaoying Xing1, Yongye Chen1, Ke Liu1, Weili Zhao1, Xiaoguang Cheng3, Shuqiang Jiang2, and Ning Lang1
1Peking University Third Hospital, Beijing, China, 2Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China, 3Department of Radiology, Beijing Jishuitan Hospital, Beijing, China

Synopsis

Keywords: Joints, SegmentationSegmentation 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.

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