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Paper Abstract and Keywords
Presentation 2020-09-03 10:00
Lung region segmentation of thoracoscopic image with unsupervised image translation
Jumpei Nitta, Megumi Nakao (Kyoto Univ.), Keiho Imanishi (e-Growth Co. Ltd.), Tetsuya Matsuda (Kyoto Univ.) MI2020-19
Abstract (in Japanese) (See Japanese page) 
(in English) In endoscopic surgery, it is necessary to understand the three-dimensional structure of the target region to improve safety. For organs that do not deform much during surgery, preoperative CT images can be used to understand their three-dimensional structure, however, deformation estimation is necessary for organs that deform substantially. Even though the intraoperative deformation estimation of organs has been widely studied, two-dimensional organ region segmentations from camera images are necessary to perform this estimation. In this paper, we performed lung region segmentation method using U-net for the thoracoscopic image, which is an organ that deforms substantially during surgery. To solve the problem of low segmentation accuracy of smoker thoracoscopic images, we performed unsupervised image translation using a CycleGAN, which we added a regularization term to the loss function. This presentation reports the image translation results and its effect of lung region segmentation.
Keyword (in Japanese) (See Japanese page) 
(in English) unsupervised learning / lung region segmentation / generative adversarial network / thoracoscopic images / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 156, MI2020-19, pp. 13-18, Sept. 2020.
Paper # MI2020-19 
Date of Issue 2020-08-27 (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 MI2020-19

Conference Information
Committee MI  
Conference Date 2020-09-03 - 2020-09-03 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Image Analysis 
Paper Information
Registration To MI 
Conference Code 2020-09-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Lung region segmentation of thoracoscopic image with unsupervised image translation 
Sub Title (in English)  
Keyword(1) unsupervised learning  
Keyword(2) lung region segmentation  
Keyword(3) generative adversarial network  
Keyword(4) thoracoscopic images  
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1st Author's Name Jumpei Nitta  
1st Author's Affiliation Kyoto University (Kyoto Univ.)
2nd Author's Name Megumi Nakao  
2nd Author's Affiliation Kyoto University (Kyoto Univ.)
3rd Author's Name Keiho Imanishi  
3rd Author's Affiliation e-Growth Co. Ltd. (e-Growth Co. Ltd.)
4th Author's Name Tetsuya Matsuda  
4th Author's Affiliation Kyoto University (Kyoto Univ.)
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Speaker Author-1 
Date Time 2020-09-03 10:00:00 
Presentation Time 15 minutes 
Registration for MI 
Paper # MI2020-19 
Volume (vol) vol.120 
Number (no) no.156 
Page pp.13-18 
#Pages 6 
Date of Issue 2020-08-27 (MI) 

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