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

Wang, Haolin; Ou, Yafei; Fang, Wanxuan; Ambalathankandy, Prasoon; Goto, Naoto; Ota, Gen; Okino, Taichi; Fukae, Jun; Sutherland, Kenneth; Ikebe, Masayuki; Kamishima, Tamotsu

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

Wang, Haolin; Ou, Yafei; Fang, Wanxuan et al.

Computerized Medical Imaging and Graphics, 2023, 108, 102273

Permalink : https://hdl.handle.net/2115/92807

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論文情報

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アクセス権	open access
URI	https://hdl.handle.net/2115/92807
タイトル	A deep registration method for accurate quantification of joint space narrowing progression in rheumatoid arthritis
著者	Wang, Haolin ; Ou, Yafei ; Fang, Wanxuan ; Ambalathankandy, Prasoon ; Goto, Naoto ; Ota, Gen ; Okino, Taichi ; Fukae, Jun ; Sutherland, Kenneth ; Ikebe, Masayuki ; Kamishima, Tamotsu
著者名 Wang, Haolin
著者名 Ou, Yafei
著者名 Fang, Wanxuan
著者名 Ambalathankandy, Prasoon
著者名 Goto, Naoto
著者名 Ota, Gen
著者名 Okino, Taichi
著者名 Fukae, Jun
e-Rad_Researcher 30451446 著者名 Sutherland, Kenneth
e-Rad_Researcher 20374613 著者名 Ikebe, Masayuki
e-Rad_Researcher 10399868 著者名 Kamishima, Tamotsu
言語	英語
キーワード	Rheumatoid arthritis
	Joint space narrowing
	Image registration
	Deep learning
	Radiology
	Computer-aided diagnosis
発行日	2023-09
出版者	Elsevier
収録物名	Computerized Medical Imaging and Graphics
巻(号)	108
ページ	102273
抄録	Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease that leads to progressive articular destruction and severe disability. Joint space narrowing (JSN) has been regarded as an important indicator for RA progression and has received significant attention. Radiology plays a crucial role in the diagnosis and monitoring of RA through the assessment of joint space. A new framework for monitoring joint space by quantifying joint space narrowing (JSN) progression through image registration in radiographic images has emerged as a promising research direction. This framework offers the advantage of high accuracy; however, challenges still exist in reducing mismatches and improving reliability. In this work, we utilize a deep intra-subject rigid registration network to automatically quantify JSN progression in the early stages of RA. In our experiments, the mean-square error of the Euclidean distance between the moving and fixed images was 0.0031, the standard deviation was 0.0661 mm and the mismatching rate was 0.48%. Our method achieves sub-pixel level accuracy, surpassing manual measurements significantly. The proposed method is robust to noise, rotation and scaling of joints. Moreover, it provides misalignment visualization, which can assist radiologists and rheumatologists in assessing the reliability of quantification, exhibiting potential for future clinical applications. As a result, we are optimistic that our proposed method will make a significant contribution to the automatic quantification of JSN progression in RA. Code is available at https://github.com/pokeblow/Deep-Registration-QJSN-Finger.git.
資源タイプ	学術雑誌論文
出版タイプ	Accepted Manuscript
権利情報	Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
権利情報	© <2023>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
PISSN	0895-6111
関連情報 (isVersionOf)	出版社版DOI https://doi.org/10.1016/j.compmedimag.2023.102273