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Paper Abstract and Keywords
Presentation 2023-03-07 17:29
[Short Paper] High Accuracy Segmentation of trachea and bronchus using 3D U-Net
Tatsuya Ogasa, Rikuto Kuroda, Yoshiki Kawata, Hidenobu Suzuki (Tokushima Univ), Yuzi Matsumoto, Takaki Tsuchida, Masahiko Kusumoto (NCC), Noboru Niki (Medical Science Institute Inc.) MI2022-128
Abstract (in Japanese) (See Japanese page) 
(in English) High-Accuracy trachea and bronchus segmentation is required for lymph node analysis in lung cancer. Since manual segmentation is time-consuming, highly accurate automatic trachea and bronchus segmentation is required. In this paper, Res3D U-Net is used to segmentation trachea, bronchus, and lymph nodes from 3D CT images. The labels of trachea, bronchus, and lymph nodes for training are created in two steps in a human-in-the-loop workflow to improve efficiency. Next, the training data is used to train Res3D U-Net, and the automatic segmentation results are evaluated to demonstrate the effectiveness of the training data creation method.
Keyword (in Japanese) (See Japanese page) 
(in English) airway / lymph-node / segmentation / deep learning / U-Net / Human-in-the-loop / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 417, MI2022-128, pp. 217-220, March 2023.
Paper # MI2022-128 
Date of Issue 2023-02-27 (MI) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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)
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Conference Information
Committee MI  
Conference Date 2023-03-06 - 2023-03-07 
Place (in Japanese) (See Japanese page) 
Place (in English) OKINAWA SEINENKAIKAN 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MI 
Conference Code 2023-03-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) High Accuracy Segmentation of trachea and bronchus using 3D U-Net 
Sub Title (in English)  
Keyword(1) airway  
Keyword(2) lymph-node  
Keyword(3) segmentation  
Keyword(4) deep learning  
Keyword(5) U-Net  
Keyword(6) Human-in-the-loop  
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Keyword(8)  
1st Author's Name Tatsuya Ogasa  
1st Author's Affiliation Tokushima University (Tokushima Univ)
2nd Author's Name Rikuto Kuroda  
2nd Author's Affiliation Tokushima University (Tokushima Univ)
3rd Author's Name Yoshiki Kawata  
3rd Author's Affiliation Tokushima University (Tokushima Univ)
4th Author's Name Hidenobu Suzuki  
4th Author's Affiliation Tokushima University (Tokushima Univ)
5th Author's Name Yuzi Matsumoto  
5th Author's Affiliation National Cancer Center Hospital (NCC)
6th Author's Name Takaki Tsuchida  
6th Author's Affiliation National Cancer Center Hospital (NCC)
7th Author's Name Masahiko Kusumoto  
7th Author's Affiliation National Cancer Center Hospital (NCC)
8th Author's Name Noboru Niki  
8th Author's Affiliation Medical Science Institute Inc. (Medical Science Institute Inc.)
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Speaker Author-1 
Date Time 2023-03-07 17:29:00 
Presentation Time 13 minutes 
Registration for MI 
Paper # MI2022-128 
Volume (vol) vol.122 
Number (no) no.417 
Page pp.217-220 
#Pages
Date of Issue 2023-02-27 (MI) 


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