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Paper Abstract and Keywords
Presentation 2022-02-21 15:35
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 (Tokyo Tech)
Abstract (in Japanese) (See Japanese page) 
(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) 
(in English) deep learning / small-sample-size / medical image / semantic segmentation / / / /  
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Conference Information
Committee AIT ME MMS IEICE-IE IEICE-ITS  
Conference Date 2022-02-21 - 2022-02-22 
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Paper Information
Registration To IEICE-IE 
Conference Code 2022-02-IE-ITS-AIT-ME-MMS 
Language English 
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  
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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)
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Speaker Author-1 
Date Time 2022-02-21 15:35:00 
Presentation Time 15 minutes 
Registration for IEICE-IE 
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Volume (vol) vol.46 
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