Paper Abstract and Keywords |
Presentation |
2017-03-21 10:00
[Short Paper]
Bile duct segmentation from 3D CT image based on machine learning and probability map-assisted region growing Pengfei Chen, Hiroshi Tanaka, Masahiro Oda, Holger Roth, Tsuyoshi Igami, Masato Nagino, Kensaku Mori (NU) BioX2016-55 PRMU2016-218 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In this paper, we present our study on the bile duct segmentation from 3D CT volumes. In hepatobiliary surgery, it is required to know the spatial structure of the bile duct in advance. In our segmentation method, we introduce a region growing method assisted by probability map obtained from machine learning classification. At the first stage of our method, each voxel is classified as a voxel of the bile duct or not by the support vector machine. By using Platt's probabilistic outputs for support vector machines, we can acquire a probability map of the bile duct. At the second stage, we utilize the probability map to conduct a probability map-assisted region growing procedure to get the final segmentation result. In our experiments, the region growing procedure improved bile duct segmentation significantly (p=0.059). F-score increased from 0.55 to 0.58 by using the procedure. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
bile duct / segmentation / support vector machine / probability map / region growing / / / |
Reference Info. |
IEICE Tech. Rep., vol. 116, no. 528, PRMU2016-218, pp. 135-136, March 2017. |
Paper # |
PRMU2016-218 |
Date of Issue |
2017-03-13 (BioX, PRMU) |
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) |
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BioX2016-55 PRMU2016-218 |
Conference Information |
Committee |
PRMU BioX |
Conference Date |
2017-03-20 - 2017-03-21 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
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Topics (in Japanese) |
(See Japanese page) |
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Paper Information |
Registration To |
PRMU |
Conference Code |
2017-03-PRMU-BioX |
Language |
English (Japanese title is available) |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Bile duct segmentation from 3D CT image based on machine learning and probability map-assisted region growing |
Sub Title (in English) |
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Keyword(1) |
bile duct |
Keyword(2) |
segmentation |
Keyword(3) |
support vector machine |
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probability map |
Keyword(5) |
region growing |
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1st Author's Name |
Pengfei Chen |
1st Author's Affiliation |
Nogoya University (NU) |
2nd Author's Name |
Hiroshi Tanaka |
2nd Author's Affiliation |
Nogoya University (NU) |
3rd Author's Name |
Masahiro Oda |
3rd Author's Affiliation |
Nogoya University (NU) |
4th Author's Name |
Holger Roth |
4th Author's Affiliation |
Nogoya University (NU) |
5th Author's Name |
Tsuyoshi Igami |
5th Author's Affiliation |
Nogoya University (NU) |
6th Author's Name |
Masato Nagino |
6th Author's Affiliation |
Nogoya University (NU) |
7th Author's Name |
Kensaku Mori |
7th Author's Affiliation |
Nogoya University (NU) |
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Speaker |
Author-1 |
Date Time |
2017-03-21 10:00:00 |
Presentation Time |
15 minutes |
Registration for |
PRMU |
Paper # |
BioX2016-55, PRMU2016-218 |
Volume (vol) |
vol.116 |
Number (no) |
no.527(BioX), no.528(PRMU) |
Page |
pp.135-136 |
#Pages |
2 |
Date of Issue |
2017-03-13 (BioX, PRMU) |
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