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Paper Abstract and Keywords
Presentation 2019-01-22 13:20
Deep learning-based segmentation of head anatomical structures using multi-modal images -- Segmentation accuracy validation for training on a small amount of image data --
Takaaki Sugino, Holger R. Roth, Masahiro Oda (Nagoya Univ.), Taichi kin (Univ. of Tokyo), Kensaku Mori (Nagoya Univ.) MI2018-77
Abstract (in Japanese) (See Japanese page) 
(in English) This paper proposes a fully convolutional network-based method for segmenting head anatomical structures from multi-modal images to construct a database of elaborate head anatomical models for practical neurosurgery simulation.
In this study, to ease difficulties in creating annotated data for FCN training, we aim to construct a method to achieve accurate segmentation of head anatomical structures, using FCN designed to obtain multi-scale image features effective for multi-class segmentation, from less training data.
On the bases of multi-modal images of 5 brain aneurysm cases, we validated the performance of the proposed method about segmentation of head anatomical structures from training on sparse annotation data.
From the validation results, we found that the proposed method could achieve the segmentation accuracy of more than 80 % even from training on 10 % of all images for network training.
Keyword (in Japanese) (See Japanese page) 
(in English) Segmentation / deep learning / head anatomical structures / sparse annotation / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 412, MI2018-77, pp. 65-70, Jan. 2019.
Paper # MI2018-77 
Date of Issue 2019-01-15 (MI) 
ISSN Print edition: ISSN 0913-5685  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 2019-01-22 - 2019-01-23 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Image Engineering, Analysis, Recognition, etc. 
Paper Information
Registration To MI 
Conference Code 2019-01-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Deep learning-based segmentation of head anatomical structures using multi-modal images 
Sub Title (in English) Segmentation accuracy validation for training on a small amount of image data 
Keyword(1) Segmentation  
Keyword(2) deep learning  
Keyword(3) head anatomical structures  
Keyword(4) sparse annotation  
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1st Author's Name Takaaki Sugino  
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 Taichi kin  
4th Author's Affiliation The University of Tokyo (Univ. of Tokyo)
5th Author's Name Kensaku Mori  
5th Author's Affiliation Nagoya University (Nagoya Univ.)
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Speaker
Date Time 2019-01-22 13:20:00 
Presentation Time 50 
Registration for MI 
Paper # IEICE-MI2018-77 
Volume (vol) IEICE-118 
Number (no) no.412 
Page pp.65-70 
#Pages IEICE-6 
Date of Issue IEICE-MI-2019-01-15 


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