Information: Join today and make your research activities more affordable! Technical workshop participation fees and annual registration fees are available at member rates.
Notice: [Important] Announcement of Changes to Registration Fee Payment and Manuscript Upload Procedures for IEICE Technical Meetings[Type 1 Only]
Emergency Maintenance from Oct. 22, 2026: The system function will be limited. We apologize for the inconvenience.
IEICE Technical Committee Submission System
Conference Paper's Information		 Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2017-01-18 16:35
Deep-Learning-Based Segmentation for the Head Sectioned Images of the Visible Korean Project
Mohammad Eshghi, Holger R. Roth, Hirohisa Oda, Masahiro Oda (Nagoya Univ.), Min Suk Chung (Ajou Univ.), Kensaku Mori (Nagoya Univ.) MI2016-119
Abstract (in Japanese) (See Japanese page) 
(in English) This paper presents the first end-to-end pixelwise fully automated segmentation of the head sectioned images of the Visible Korean Human (VKH) Project based on Deep Convolutional Neural Networks (DCNN). By converting classification networks into Fully Convolutional Networks (FCN), a coarse map, with smaller size than the original input image, for segmentation can be created. To refine this map and obtain a pixel-dense output, standard FCNs use deconvolution layer to bilinearly upsample this coarse map. However, upsampling based on deconvolution increases the number of network parameters. To substantially expand the receptive fields or fields of view, being used for calculating the activation maps, without dramatically increasing the numbers of network parameters or filter weights, dilated convolution has been introduced recently. We used both standard FCN and dilated-convolution-based FCN for semantic segmentation of the head images. Qualitative results showed the advantage of using dilated convolutions compared to standard fully convolutional networks.
Keyword (in Japanese) (See Japanese page) 
(in English) Semantic Segmentation / Convolutional Neural Networks (CNN) / Dilated Convolution / Deep Learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 393, MI2016-119, pp. 191-194, Jan. 2017.
Paper # MI2016-119 
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 MI2016-119

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 English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Deep-Learning-Based Segmentation for the Head Sectioned Images of the Visible Korean Project 
Sub Title (in English)  
Keyword(1) Semantic Segmentation  
Keyword(2) Convolutional Neural Networks (CNN)  
Keyword(3) Dilated Convolution  
Keyword(4) Deep Learning  
Keyword(5)  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Mohammad Eshghi  
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 Hirohisa Oda  
3rd Author's Affiliation Nagoya University (Nagoya Univ.)
4th Author's Name Masahiro Oda  
4th Author's Affiliation Nagoya University (Nagoya Univ.)
5th Author's Name Min Suk Chung  
5th Author's Affiliation Ajou University (Ajou Univ.)
6th Author's Name Kensaku Mori  
6th Author's Affiliation Nagoya University (Nagoya Univ.)
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
21st Author's Name  
21st Author's Affiliation ()
22nd Author's Name  
22nd Author's Affiliation ()
23rd Author's Name  
23rd Author's Affiliation ()
24th Author's Name  
24th Author's Affiliation ()
25th Author's Name  
25th Author's Affiliation ()
26th Author's Name / /
26th Author's Affiliation ()
()
27th Author's Name / /
27th Author's Affiliation ()
()
28th Author's Name / /
28th Author's Affiliation ()
()
29th Author's Name / /
29th Author's Affiliation ()
()
30th Author's Name / /
30th Author's Affiliation ()
()
31st Author's Name / /
31st Author's Affiliation ()
()
32nd Author's Name / /
32nd Author's Affiliation ()
()
33rd Author's Name / /
33rd Author's Affiliation ()
()
34th Author's Name / /
34th Author's Affiliation ()
()
35th Author's Name / /
35th Author's Affiliation ()
()
36th Author's Name / /
36th Author's Affiliation ()
()
Speaker Author-1 
Date Time 2017-01-18 16:35:00 
Presentation Time 12 minutes 
Registration for MI 
Paper # MI2016-119 
Volume (vol) vol.116 
Number (no) no.393 
Page pp.191-194 
#Pages 4 
Date of Issue 2017-01-11 (MI) 

[Return to Top Page]

[Return to IEICE Web Page]

(Line Height for English Pages: 1)
The Institute of Electronics, Information and Communication Engineers (IEICE), Japan