A deep registration method for accurate quantification of joint space narrowing progression in rheumatoid arthritis
Copyright © 2023. Published by Elsevier Ltd..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:108 |
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| Enthalten in: |
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society - 108(2023) vom: 10. Sept., Seite 102273 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Haolin [VerfasserIn] |
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| Links: |
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| Themen: |
Computer-aided diagnosis |
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| Anmerkungen: |
Date Completed 04.09.2023 Date Revised 05.09.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.compmedimag.2023.102273 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM360315585 |
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| 245 | 1 | 2 | |a A deep registration method for accurate quantification of joint space narrowing progression in rheumatoid arthritis |
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| 520 | |a Copyright © 2023. Published by Elsevier Ltd. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Computer-aided diagnosis | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Image registration | |
| 650 | 4 | |a Joint space narrowing | |
| 650 | 4 | |a Radiology | |
| 650 | 4 | |a Rheumatoid arthritis | |
| 700 | 1 | |a Ou, Yafei |e verfasserin |4 aut | |
| 700 | 1 | |a Fang, Wanxuan |e verfasserin |4 aut | |
| 700 | 1 | |a Ambalathankandy, Prasoon |e verfasserin |4 aut | |
| 700 | 1 | |a Goto, Naoto |e verfasserin |4 aut | |
| 700 | 1 | |a Ota, Gen |e verfasserin |4 aut | |
| 700 | 1 | |a Okino, Taichi |e verfasserin |4 aut | |
| 700 | 1 | |a Fukae, Jun |e verfasserin |4 aut | |
| 700 | 1 | |a Sutherland, Kenneth |e verfasserin |4 aut | |
| 700 | 1 | |a Ikebe, Masayuki |e verfasserin |4 aut | |
| 700 | 1 | |a Kamishima, Tamotsu |e verfasserin |4 aut | |
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