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Paper Abstract and Keywords
Presentation 2019-01-22 15:35
Segmentation of lung nodules on 3D CT images by using DeconvNet and V-Net
Shunsuke Kidera, Shoji Kido, Yasushi Hirano (Yamaguchi Univ.), Nobuyuki Tanaka (Saiseikai Hosp) MI2018-85
Abstract (in Japanese) (See Japanese page) 
(in English) Semantic segmentation of lung nodules is important for texture analysis. However, manual segmentation needs a lot of time because the number of slices in CT images are huge. In this study, we segmented lung nodules on 3D CT images by use of DeconvNet and V-Net. In our experiment, we compared the performance of two loss functions named Cross Entropy and Dice Loss. The best performance in our study was 0.810±0.014 of dice index by using V-Net and Dice Loss.
Keyword (in Japanese) (See Japanese page) 
(in English) lung nodule / Deep learning / Segmentation / 3D CT images / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 412, MI2018-85, pp. 103-106, Jan. 2019.
Paper # MI2018-85 
Date of Issue 2019-01-15 (MI) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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. (No. 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF MI2018-85

Conference Information
Committee MI  
Conference Date 2019-01-22 - 2019-01-23 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Image Engineering, Analysis, Recognition, etc. 
Paper Information
Registration To MI 
Conference Code 2019-01-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Segmentation of lung nodules on 3D CT images by using DeconvNet and V-Net 
Sub Title (in English)  
Keyword(1) lung nodule  
Keyword(2) Deep learning  
Keyword(3) Segmentation  
Keyword(4) 3D CT images  
1st Author's Name Shunsuke Kidera  
1st Author's Affiliation Yamaguchi University (Yamaguchi Univ.)
2nd Author's Name Shoji Kido  
2nd Author's Affiliation Yamaguchi University (Yamaguchi Univ.)
3rd Author's Name Yasushi Hirano  
3rd Author's Affiliation Yamaguchi University (Yamaguchi Univ.)
4th Author's Name Nobuyuki Tanaka  
4th Author's Affiliation Saiseikai Yamaguchi General Hospital (Saiseikai Hosp)
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Date Time 2019-01-22 15:35:00 
Presentation Time 15 
Registration for MI 
Paper # IEICE-MI2018-85 
Volume (vol) IEICE-118 
Number (no) no.412 
Page pp.103-106 
#Pages IEICE-4 
Date of Issue IEICE-MI-2019-01-15 

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