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| Presentation | 2014-01-26 15:45 A Preliminary Study on Organ Segmentation using Conditional Random Fields from Medical Image Yukitaka Nimura, Yuichiro Hayashi (Nagoya Univ.), Takayuki Kitasaka (Aichi Inst. of Tech.), Kensaku Mori (Nagoya Univ.)  MI2013-84 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This paper describes an organ region segmentation method using conditional random fields from medical images. A lot of methods have been proposed to enable automated extraction of organ regions from medical images. However, it is necessary to adjust emperical parameters of them to obtain precise organ regions. In this paper, we propose an organ segmentation method using structured output learning which is based on probabilistic graphical model. The proposed method utilizes randomly connected conditional random fields as probabilistic graphical model and binary features which represent the relationship between voxel intensities and organ labels. Also we optimize the weight parameters of conditional random fields using stochastic gradient descent and estimate organ labels using maximum a posteriori estimation. The experimental result revealed that the proposed method can extract organ regions automatically using structured output learning. The error of organ label estimation was 10.0%. DICE coefficients of liver, spleen, right kidney, left kidney, and pancreas are 0.65, 0.61, 0.61, 0.63, 0.24, respectively. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | structured output learning / conditional random fields / segmentation / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 113, no. 410, MI2013-84, pp. 155-160, Jan. 2014. | |
| Paper # | MI2013-84 | |
| Date of Issue | 2014-01-19 (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 | MI2013-84 |
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| Conference Information | |
| Committee | MI |
| Conference Date | 2014-01-26 - 2014-01-27 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Bunka Tenbusu Kan |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Computer Assisted Diagnosis and Therapy Based on Computational Anatomy, etc. |
| Paper Information | |
| Registration To | MI |
| Conference Code | 2014-01-MI |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Preliminary Study on Organ Segmentation using Conditional Random Fields from Medical Image |
| Sub Title (in English) | |
| Keyword(1) | structured output learning |
| Keyword(2) | conditional random fields |
| Keyword(3) | segmentation |
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| 1st Author's Name | Yukitaka Nimura |
| 1st Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 2nd Author's Name | Yuichiro Hayashi |
| 2nd Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 3rd Author's Name | Takayuki Kitasaka |
| 3rd Author's Affiliation | Aichi Institute of Technology (Aichi Inst. of Tech.) |
| 4th Author's Name | Kensaku Mori |
| 4th Author's Affiliation | Nagoya University (Nagoya Univ.) |
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| Speaker | Author-1 |
| Date Time | 2014-01-26 15:45:00 |
| Presentation Time | 15 minutes |
| Registration for | MI |
| Paper # | MI2013-84 |
| Volume (vol) | vol.113 |
| Number (no) | no.410 |
| Page | pp.155-160 |
| #Pages | 6 |
| Date of Issue | 2014-01-19 (MI) |
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