| Presentation | 2020-09-03 Lung region segmentation of thoracoscopic image with unsupervised image translation Jumpei Nitta, Megumi Nakao, Keiho Imanishi, Tetsuya Matsuda, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In endoscopic surgery, it is necessary to understand the three-dimensional structure of the target region to improve safety. For organs that do not deform much during surgery, preoperative CT images can be used to understand their three-dimensional structure, however, deformation estimation is necessary for organs that deform substantially. Even though the intraoperative deformation estimation of organs has been widely studied, two-dimensional organ region segmentations from camera images are necessary to perform this estimation. In this paper, we performed lung region segmentation method using U-net for the thoracoscopic image, which is an organ that deforms substantially during surgery. To solve the problem of low segmentation accuracy of smoker thoracoscopic images, we performed unsupervised image translation using a CycleGAN, which we added a regularization term to the loss function. This presentation reports the image translation results and its effect of lung region segmentation. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | unsupervised learning / lung region segmentation / generative adversarial network / thoracoscopic images |
| Paper # | MI2020-19 |
| Date of Issue | 2020-08-27 (MI) |
| Conference Information | |
| Committee | MI |
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| Conference Date | 2020/9/3(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Medical Image Analysis |
| Chair | Yoshiki Kawata(Tokushima Univ.) |
| Vice Chair | Takayuki Kitasaka(Aichi Inst. of Tech.) / Hidekata Hontani(Nagoya Inst. of Tech.) |
| Secretary | Takayuki Kitasaka(Yamaguchi Univ.) / Hidekata Hontani(Univ. of Hyogo) |
| Assistant | Hotaka Takizawa(Tsukuba Univ.) / Yoshito Otake(NAIST) |
| Paper Information | |
| Registration To | Technical Committee on Medical Imaging |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Lung region segmentation of thoracoscopic image with unsupervised image translation |
| Sub Title (in English) | |
| Keyword(1) | unsupervised learning |
| Keyword(2) | lung region segmentation |
| Keyword(3) | generative adversarial network |
| Keyword(4) | thoracoscopic images |
| 1st Author's Name | Jumpei Nitta |
| 1st Author's Affiliation | Kyoto University(Kyoto Univ.) |
| 2nd Author's Name | Megumi Nakao |
| 2nd Author's Affiliation | Kyoto University(Kyoto Univ.) |
| 3rd Author's Name | Keiho Imanishi |
| 3rd Author's Affiliation | e-Growth Co. Ltd.(e-Growth Co. Ltd.) |
| 4th Author's Name | Tetsuya Matsuda |
| 4th Author's Affiliation | Kyoto University(Kyoto Univ.) |
| Date | 2020-09-03 |
| Paper # | MI2020-19 |
| Volume (vol) | vol.120 |
| Number (no) | MI-156 |
| Page | pp.pp.13-18(MI), |
| #Pages | 6 |
| Date of Issue | 2020-08-27 (MI) |