A deep registration method for accurate quantification of joint space narrowing progression in rheumatoid arthritis

• DOI: 10.1016/j.compmedimag.2023.102273
• Publication Date: 2023-07-22T07:03:42Z
• Authors: Haolin Wang; Yafei Ou; Wanxuan Fang; Prasoon Ambalatha...; Naoto Goto; Gen Ota; Taichi Okino; Jun Fukae; Kenneth Sutherland; Masayuki Ikebe; Tamotsu Kamishima
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; I.4; Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Computer Science - Computer Vision and Pattern Recognition; Reproducibility of Results; Electrical Engineering and Systems Science - Image and Video Processing; 68T45; 3. Good health; Machine Learning (cs.LG); Arthritis, Rheumatoid; Radiography; Disease Progression; FOS: Electrical engineering, electronic engineering, information engineering; Humans

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease that results in progressive articular destruction and severe disability. Joint space narrowing (JSN) progression has been regarded as an important indicator for RA progression and has received sustained attention. In the diagnosis and monitoring of RA, radiology plays a crucial role to monitor joint space. A new framework for monitoring joint space by quantifying JSN progression through image registration in radiographic images has been developed. This framework offers the advantage of high accuracy, however, challenges do exist in reducing mismatches and improving reliability. In this work, a deep intra-subject rigid registration network is proposed to automatically quantify JSN progression in the early stage of RA. In our experiments, the mean-square error of Euclidean distance between moving and fixed image is 0.0031, standard deviation is 0.0661 mm, and the mismatching rate is 0.48\%. The proposed method has sub-pixel level accuracy, exceeding manual measurements by far, and is equipped with immune to noise, rotation, and scaling of joints. Moreover, this work provides loss visualization, which can aid radiologists and rheumatologists in assessing quantification reliability, with important implications for possible future clinical applications. As a result, we are optimistic that this proposed work will make a significant contribution to the automatic quantification of JSN progression in RA.<br/>11 pages, 9 figures, 7 tables<br/>