| 講演抄録／キーワード |
| 講演名 |
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） |
| 抄録 |
（和） |
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. |
| （英） |
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. |
| キーワード |
（和） |
deep learning / small-sample-size / medical image / semantic segmentation / / / / |
| （英） |
deep learning / small-sample-size / medical image / semantic segmentation / / / / |
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映情学技報 |
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