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Paper Abstract and Keywords
Presentation 2022-09-15 10:00
Automatic Multi-Measure Classification of Hip Osteoarthritis Based on Digitally-Reconstructed Radiographs using Deep Learning
Masachika Masuda, Mazen Soufi, Yoshito Otake (NAIST), Keisuke Uemura (Osaka Univ.), Masaki Takao (Ehime Univ.), Nobuhiko Sugano (Osaka Univ.), Yoshinobu Sato (NAIST) MI2022-49
Abstract (in Japanese) (See Japanese page) 
(in English) Hip Osteoarthritis (HOA) is usually diagnosed by radiographs. In addition to the degree of cartilage degeneration, the degree of dislocation of the femoral head from the acetabulum (subluxation or dislocation) should also be evaluated. Recently, an automatic classification method using Convolutional Neural Networks (CNNs) has been reported; however, HOA diagnosis was addressed as a binary classification problem, therefore it was not able to represent clinically important changes in shape and intensity values with the disease progression. Therefore, the purpose of this study was to develop an automated HOA classification approach that incorporates information on disease progression based on Digitally-Reconstructed Radiographs (DRRs). The novelty is that it simultaneously classifies each DRR into two diagnostic measures, i.e., the Crowe classification (degree of dislocation) and the Kellgren-Lawrence score (OA severity), to represent clinically important changes. Three deep learning-based classifiers, i.e. VGG, DenseNet and ViT, were evaluated. The impact of involving dropout sampling at test-time for model uncertainty estimation was also evaluated. The ViT performance was superior to the other classifiers in terms of the two-measure classification accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep Learning / Hip Osteoarthritis / Uncertainty / / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 188, MI2022-49, pp. 1-4, Sept. 2022.
Paper # MI2022-49 
Date of Issue 2022-09-08 (MI) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF MI2022-49

Conference Information
Committee MI  
Conference Date 2022-09-15 - 2022-09-15 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To MI 
Conference Code 2022-09-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Automatic Multi-Measure Classification of Hip Osteoarthritis Based on Digitally-Reconstructed Radiographs using Deep Learning 
Sub Title (in English)  
Keyword(1) Deep Learning  
Keyword(2) Hip Osteoarthritis  
Keyword(3) Uncertainty  
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1st Author's Name Masachika Masuda  
1st Author's Affiliation Nara Institute of Science and Technology (NAIST)
2nd Author's Name Mazen Soufi  
2nd Author's Affiliation Nara Institute of Science and Technology (NAIST)
3rd Author's Name Yoshito Otake  
3rd Author's Affiliation Nara Institute of Science and Technology (NAIST)
4th Author's Name Keisuke Uemura  
4th Author's Affiliation Osaka University (Osaka Univ.)
5th Author's Name Masaki Takao  
5th Author's Affiliation Ehime University (Ehime Univ.)
6th Author's Name Nobuhiko Sugano  
6th Author's Affiliation Osaka University (Osaka Univ.)
7th Author's Name Yoshinobu Sato  
7th Author's Affiliation Nara Institute of Science and Technology (NAIST)
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Speaker Author-1 
Date Time 2022-09-15 10:00:00 
Presentation Time 15 minutes 
Registration for MI 
Paper # MI2022-49 
Volume (vol) vol.122 
Number (no) no.188 
Page pp.1-4 
#Pages 4 
Date of Issue 2022-09-08 (MI) 

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