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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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
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
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)