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Paper Abstract and Keywords
Presentation 2017-01-18 11:22
Automated abdominal lymph node detection from 3D CT volumes using Structured Random Forest
Yutaka Hoshiyama, Holger R. Roth, Masahiro Oda (Nagoya Univ.), Yoshihiko Nakamura (NIT), Kazunari Misawa (Aichi Cancer Center Hospital), Michitaka Fujiwara, Kensaku Mori (Nagoya Univ.) MI2016-76
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we report a study on automated lymph node detection method from 3D abdominal CT volumes using the Structured Random Forest (SRF).
SRF is an extended method of the Random Forest that can learn the structure of the image.
Traditional lymph node detection methods were mainly based on filter-based approaches.
They performed detection by finding the blob-like structure under the assumption that lymph nodes have spherical shape.
Though their detection rates were high, they cause many false positives (FP) because there are a number of non-lymph node objects who have blob-like structures on CT volumes.
Therefore, we perform the structure learning instead of detecting a blob-like structure, and aim to improve detection accuracy.
We use feature values based on various intensity including output image of blob-like structure enhancement filter.
The proposed method showed 60.0% for the detection rate and 45/case for the FPs when we also perform FP reduction using SVM.
Keyword (in Japanese) (See Japanese page) 
(in English) Medical image processing / Lymph node detection / Structured Random Forest / / / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 393, MI2016-76, pp. 23-28, Jan. 2017.
Paper # MI2016-76 
Date of Issue 2017-01-11 (MI) 
ISSN Print edition: ISSN 0913-5685    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 MI2016-76

Conference Information
Committee MI  
Conference Date 2017-01-18 - 2017-01-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Tenbusu Naha 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Image Engineering, Analysis, Recognition, etc. 
Paper Information
Registration To MI 
Conference Code 2017-01-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Automated abdominal lymph node detection from 3D CT volumes using Structured Random Forest 
Sub Title (in English)  
Keyword(1) Medical image processing  
Keyword(2) Lymph node detection  
Keyword(3) Structured Random Forest  
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1st Author's Name Yutaka Hoshiyama  
1st Author's Affiliation Nagoya University (Nagoya Univ.)
2nd Author's Name Holger R. Roth  
2nd Author's Affiliation Nagoya University (Nagoya Univ.)
3rd Author's Name Masahiro Oda  
3rd Author's Affiliation Nagoya University (Nagoya Univ.)
4th Author's Name Yoshihiko Nakamura  
4th Author's Affiliation Tomakomai College (NIT)
5th Author's Name Kazunari Misawa  
5th Author's Affiliation Aichi Cancer Center Hospital (Aichi Cancer Center Hospital)
6th Author's Name Michitaka Fujiwara  
6th Author's Affiliation Nagoya University (Nagoya Univ.)
7th Author's Name Kensaku Mori  
7th Author's Affiliation Nagoya University (Nagoya Univ.)
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Speaker Author-1 
Date Time 2017-01-18 11:22:00 
Presentation Time 12 minutes 
Registration for MI 
Paper # MI2016-76 
Volume (vol) vol.116 
Number (no) no.393 
Page pp.23-28 
#Pages 6 
Date of Issue 2017-01-11 (MI) 

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