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Paper Abstract and Keywords
Presentation 2020-01-30 13:25
Bleeding area segmentation from laparoscopic video based on U-Net for laparoscopic surgery support
Shota Yamamoto, Yuichiro Hayashi, Shintaro Morimitsu, Takuya Ozawa (Nagoya Univ.), Takayuki Kitasaka (Aichi Institute of Tech.), Masahiro Oda (Nagoya Univ.), Nobuyoshi Takeshita, Masaaki Ito (Cancer Center), Kensaku Mori (Nagoya Univ.) MI2019-115
Abstract (in Japanese) (See Japanese page) 
(in English) This paper reports a bleeding region segmentation method in laparoscopic videos based on U-Net for laparoscopic surgery assistance.
Researches on recognition of surgical process have been conducted by analyzing laparoscopic videos for assisting laparoscopic surgery.
We have focused on bleeding areas during surgery and have segmented bleeding areas from laparoscopic videos using U-Net.
Since our previous method processed frame by frame in the videos, time-series smoothness was lost in the results.
In this paper, we construct U-Net which considers time series information.
In the experiment, we segmented the bleeding area in laparoscopic surgery videos by the previous method and proposed method.
The experimental results showed that the bleeding region was extracted with continuity between frames by using multiple consecutive frames as input.
Keyword (in Japanese) (See Japanese page) 
(in English) Laparoscopic surgery / Surgical process analysis / Segmentation / Deep learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 399, MI2019-115, pp. 209-214, Jan. 2020.
Paper # MI2019-115 
Date of Issue 2020-01-22 (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 MI2019-115

Conference Information
Committee MI  
Conference Date 2020-01-29 - 2020-01-30 
Place (in Japanese) (See Japanese page) 
Place (in English) OKINAWAKEN SEINENKAIKAN 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Image Engineering, Analysis, Recognition, etc. 
Paper Information
Registration To MI 
Conference Code 2020-01-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Bleeding area segmentation from laparoscopic video based on U-Net for laparoscopic surgery support 
Sub Title (in English)  
Keyword(1) Laparoscopic surgery  
Keyword(2) Surgical process analysis  
Keyword(3) Segmentation  
Keyword(4) Deep learning  
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1st Author's Name Shota Yamamoto  
1st Author's Affiliation Nagoya University (Nagoya Univ.)
2nd Author's Name Yuichiro Hayashi  
2nd Author's Affiliation Nagoya University (Nagoya Univ.)
3rd Author's Name Shintaro Morimitsu  
3rd Author's Affiliation Nagoya University (Nagoya Univ.)
4th Author's Name Takuya Ozawa  
4th Author's Affiliation Nagoya University (Nagoya Univ.)
5th Author's Name Takayuki Kitasaka  
5th Author's Affiliation Aichi Institute of Technology (Aichi Institute of Tech.)
6th Author's Name Masahiro Oda  
6th Author's Affiliation Nagoya University (Nagoya Univ.)
7th Author's Name Nobuyoshi Takeshita  
7th Author's Affiliation National Cancer Center Hospital East (Cancer Center)
8th Author's Name Masaaki Ito  
8th Author's Affiliation National Cancer Center Hospital East (Cancer Center)
9th Author's Name Kensaku Mori  
9th Author's Affiliation Nagoya University (Nagoya Univ.)
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Speaker Author-1 
Date Time 2020-01-30 13:25:00 
Presentation Time 45 minutes 
Registration for MI 
Paper # MI2019-115 
Volume (vol) vol.119 
Number (no) no.399 
Page pp.209-214 
#Pages 6 
Date of Issue 2020-01-22 (MI) 

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