| Presentation | 2022-02-21 Liver Tumor Segmentation by Using a Massive-Training Artificial Neural Network (MTANN) and its Analysis in Liver CT. Yuqiao Yang, Muneyuki Sato, Ze Jin, Kenji Suzuki, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Based on a 3D massive-training artificial neural network (MTANN) combined with a Hessian-based ellipse enhancer, a small-sample-size deep learning technique for semantic segmentation of liver tumors in contrast-enhanced CT is proposed. To show the proposed model's efficiency in a small-sample size dataset, we trained the proposed models with only 7 tumors from 7 patients, and 14 tumors from 12 patients. The proposed model achieved a Dice score of 0.703 with the training set of 12 patients. The accuracy was comparable to the CNN-based method with 131 patients in the MICCAI 2017 competition. The proposed model is essential in deep learning applications in medical imaging where a large database is not available. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | deep learning / small-sample-size / medical image / semantic segmentation |
| Paper # | ITS2021-33,IE2021-42 |
| Date of Issue | 2022-02-14 (ITS, IE) |
| Conference Information | |
| Committee | IE / ITS / ITE-AIT / ITE-ME / ITE-MMS |
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| Conference Date | 2022/2/21(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Image Processing, etc. |
| Chair | Kazuya Kodama(NII) / Masahiro Fujii(Utsunomiya Univ.) / Hisaki Nate(Tokyo Polytechnic Univ.) / Hiroyuki Arai(Nippon Inst. of Tech.) / Kenji Machida(NHK) |
| Vice Chair | Hiroyuki Bandoh(NTT) / Toshihiko Yamazaki(Univ. of Tokyo) / Kohei Ohno(Meiji Univ.) / Naohisa Hashimoto(AIST) / / Shogo Muramatsu(Niigata Univ.) |
| Secretary | Hiroyuki Bandoh(KDDI Research) / Toshihiko Yamazaki(Nagoya Inst. of Tech.) / Kohei Ohno(Akita Prefectural Univ.) / Naohisa Hashimoto(NIT, Tsuruoka College) / / Shogo Muramatsu(NHK) / (Hokkaido Univ.) |
| Assistant | Shunsuke Iwamura(NHK) / Shinobu Kudo(NTT) / Msataka Imao(Mitsubishi Electric) / Kenshi Saho(Toyama Prefectural Univ.) / Keiji Jimi(Gunma Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Image Engineering / Technical Committee on Intelligent Transport Systems Technology / Technical Group on Artistic Image Technology / Technical Group on Media Engineering / Technical Group on Multi-media Storage |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Liver Tumor Segmentation by Using a Massive-Training Artificial Neural Network (MTANN) and its Analysis in Liver CT. |
| Sub Title (in English) | |
| Keyword(1) | deep learning |
| Keyword(2) | small-sample-size |
| Keyword(3) | medical image |
| Keyword(4) | semantic segmentation |
| 1st Author's Name | Yuqiao Yang |
| 1st Author's Affiliation | Tokyo Institute of Technology(Tokyo Tech) |
| 2nd Author's Name | Muneyuki Sato |
| 2nd Author's Affiliation | Tokyo Institute of Technology(Tokyo Tech) |
| 3rd Author's Name | Ze Jin |
| 3rd Author's Affiliation | Tokyo Institute of Technology(Tokyo Tech) |
| 4th Author's Name | Kenji Suzuki |
| 4th Author's Affiliation | Tokyo Institute of Technology(Tokyo Tech) |
| Date | 2022-02-21 |
| Paper # | ITS2021-33,IE2021-42 |
| Volume (vol) | vol.121 |
| Number (no) | ITS-373,IE-374 |
| Page | pp.pp.49-54(ITS), pp.49-54(IE), |
| #Pages | 6 |
| Date of Issue | 2022-02-14 (ITS, IE) |