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| Presentation | 2020-01-29 16:20 Surgical tool segmentation from laparoscopic images using laparoscopic image syntheses and deep learning Takuya Ozawa, Yuichiro Hayashi, Hirohisa Oda, Masahiro Oda (Nagoya Univ.), Takayuki Kitasaka (Aich Ins. of Tech.), Nobuyoshi Takeshita, Masaaki Ito (NCC East), Kensaku Mori (Nagoya Univ.)  MI2019-94 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This paper proposes a surgical tool segmentation method from laparoscopic images using image synthesis and deep learning. Forceps and anatomical structure recognition in laparoscopic movies is applicable to develop various surgical assistance and surgical process analysis systems. In the analysis of laparoscopic movies using deep learning, it is difficult to collect massive training data of forceps which are used infrequently during surgery. Recognition accuracy of them may be reduced by lack of training data. This paper solves the problem by generating training data using image synthesis. A pairs of synthetic laparoscopic images and segmentation data is automatically generated by superimposing the 3D forceps models on the actual laparoscopic movies. A deep learning model of forceps segmentation is trained using both the synthetic and the manual segmentation datasets. We applied the trained model to extract forceps in laparoscopic movies. The result showed that recognition accuracy of forceps used infrequently during surgery was improved by using the proposed training data. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Laparoscopic video / Surgical tool segmentation / Image synthesis / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 399, MI2019-94, pp. 129-134, Jan. 2020. | |
| Paper # | MI2019-94 | |
| Date of Issue | 2020-01-22 (MI) | |
| ISSN | 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 | MI2019-94 |
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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) | Surgical tool segmentation from laparoscopic images using laparoscopic image syntheses and deep learning |
| Sub Title (in English) | |
| Keyword(1) | Laparoscopic video |
| Keyword(2) | Surgical tool segmentation |
| Keyword(3) | Image synthesis |
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| 1st Author's Name | Takuya Ozawa |
| 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 | 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 | Takayuki Kitasaka |
| 5th Author's Affiliation | Aichi Institute of Technology (Aich Ins. of Tech.) |
| 6th Author's Name | Nobuyoshi Takeshita |
| 6th Author's Affiliation | National Cancer Center Hospital East (NCC East) |
| 7th Author's Name | Masaaki Ito |
| 7th Author's Affiliation | National Cancer Center Hospital East (NCC East) |
| 8th Author's Name | Kensaku Mori |
| 8th Author's Affiliation | Nagoya University (Nagoya Univ.) |
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| Speaker | Author-1 |
| Date Time | 2020-01-29 16:20:00 |
| Presentation Time | 10 minutes |
| Registration for | MI |
| Paper # | MI2019-94 |
| Volume (vol) | vol.119 |
| Number (no) | no.399 |
| Page | pp.129-134 |
| #Pages | 6 |
| Date of Issue | 2020-01-22 (MI) |
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