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Paper Abstract and Keywords
Presentation 2020-01-29 09:55
Automated dection for neck and thoracic lesions on FDG-PET/CT by lesion enhancement using one-class SVM
Atsuko Tanaka, Mitsutaka Memoto, Hayato Kaida, Yuichi Kimura, Takashi Nagaoka, Takahiro Yamada, Kazuyuki Ushifusa (Kindai Uni), Kohei Hanaoka (Kindai Uni Hosp), Kazuhiro Kitajima (Hyogo Col of Med), Tatsuya Tsuchitani (Hosp of Hyogo Col of Med), Kazunari Ishii (Kindai Uni) MI2019-67
Abstract (in Japanese) (See Japanese page) 
(in English) We propose an anomaly detection based method to detect primary and metastatic lesions in the cervical and thoracic region on FDG-PET/CT by lesion enhancement using one-class SVM (OCSVM). The first step of the proposed method is the automatic extraction of bilateral lungs, cervical region, and the mediastinal region. Secondary, voxel abnormality is measured at each voxel in the extracted regions by organ-specific OCSVMs, that have been trained by normal voxel data previously. Next, lesion candidates are detected by the thresholding process for the voxel abnormalities. In the evaluation using clinical FDG-PET/CT, we confirmed the effectiveness of the proposed method by comparison of the lesion detection performances between the proposed method and our previous method using the Mahalanobis distance.
Keyword (in Japanese) (See Japanese page) 
(in English) one-class SVM / anomaly detection / FDG-PET/CT / computer aided diagnosis / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 399, MI2019-67, pp. 11-14, Jan. 2020.
Paper # MI2019-67 
Date of Issue 2020-01-22 (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 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) Automated dection for neck and thoracic lesions on FDG-PET/CT by lesion enhancement using one-class SVM 
Sub Title (in English)  
Keyword(1) one-class SVM  
Keyword(2) anomaly detection  
Keyword(3) FDG-PET/CT  
Keyword(4) computer aided diagnosis  
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1st Author's Name Atsuko Tanaka  
1st Author's Affiliation Kindai University (Kindai Uni)
2nd Author's Name Mitsutaka Memoto  
2nd Author's Affiliation Kindai University (Kindai Uni)
3rd Author's Name Hayato Kaida  
3rd Author's Affiliation Kindai University (Kindai Uni)
4th Author's Name Yuichi Kimura  
4th Author's Affiliation Kindai University (Kindai Uni)
5th Author's Name Takashi Nagaoka  
5th Author's Affiliation Kindai University (Kindai Uni)
6th Author's Name Takahiro Yamada  
6th Author's Affiliation Kindai University (Kindai Uni)
7th Author's Name Kazuyuki Ushifusa  
7th Author's Affiliation Kindai University (Kindai Uni)
8th Author's Name Kohei Hanaoka  
8th Author's Affiliation Kindai University Hospital (Kindai Uni Hosp)
9th Author's Name Kazuhiro Kitajima  
9th Author's Affiliation Hyogo College of Medicine (Hyogo Col of Med)
10th Author's Name Tatsuya Tsuchitani  
10th Author's Affiliation Hospital of Hyogo College of Medicine (Hosp of Hyogo Col of Med)
11th Author's Name Kazunari Ishii  
11th Author's Affiliation Kindai University (Kindai Uni)
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Speaker Author-1 
Date Time 2020-01-29 09:55:00 
Presentation Time 10 minutes 
Registration for MI 
Paper # MI2019-67 
Volume (vol) vol.119 
Number (no) no.399 
Page pp.11-14 
#Pages
Date of Issue 2020-01-22 (MI) 


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