Clinical application and improvement of U-net-based model for automatic segmentation of the heart(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2020年第10期
- Page:
- 1218-1223
- Research Field:
- 医学放射物理
- Publishing date:
Info
- Title:
- Clinical application and improvement of U-net-based model for automatic segmentation of the heart
- Author(s):
- CHANG Yankui1; PENG Zhao1; ZHOU Jieping1; 2; PI Yifei3; WU Haotian4; WU Aidong2; XU Xie1; 4; PEI Xi1; 4
- 1. Center of Radiological Medical Physics, University of Science and Technology of China, Hefei 230025, China 2. Department of Radiation Oncology, the First Affiliated Hospital of University of Science and Technology of China, Hefei 230001, China 3. Department of Radiation Oncology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China 4. Anhui Wisdom Technology Co. Ltd., Hefei 230088, China
- Keywords:
- U-net heart automatic segmentation Dice similarity coefficient
- PACS:
- R811.1；R318
- DOI:
- DOI:10.3969/j.issn.1005-202X.2020.10.002
- Abstract:
- Abstract: Objective To investigate the clinical applicability of the model established based on the data from different hospitals for the automatic segmentation of the heart and to discuss the methods to improve the model. Methods A network based on U-net and Inception module was firstly constructed for the automatic segmentation of the heart, and the clinical data from different hospitals were collected, including 65 cases from the First Affiliated Hospital of University of Science and Technology of China (data 1), 50 cases from MICCAI2019 match data (data 2), the mixed data of data 1 and data 2 (data 3), 50 cases from the First Affiliated Hospital of Zhengzhou University (data 4) and 100 cases from the First Affiliated Hospital of Zhengzhou University (data 5). The collected data were trained for obtaining 5 different models. Then, with the clinical data of another 59 patients from the First Affiliated Hospital of Zhengzhou University as test set, the segmentation accuracies of test set on different models were evaluated using Dice similarity coefficient (DSC), and the differences in segmentation accuracies among different models were also compared. Finally, model 3 was used as a pre-trained model of the heart, and the model was retrained with data 5. Three groups of experiments (20 cases each time × 5 times, 10 cases each time× 10 times, 5 cases each time× 20 times) were carried out to observe the improvement of the pre-trained model. Results The average DSC of the test set based on models 1 to 5 was 0.926, 0.932, 0.939, 0.941 and 0.950, respectively. During the retraining of the pre-trained model of the heart, the model was more stable in the experiment of 20 cases each time × 5 times. Conclusion The trained model established based on the data from different hospitals has different performances in the automatic segmentation of the heart, and the model trained with local hospital data has a higher prediction accuracy. For the model based on non-local data training, the retraining with local data can effectively improve the accuracy of model prediction, in which the retraining with 20 cases each time has the optimal performance.
Last Update: 2020-10-29