Application of deep convolution neural network in radiotherapy planning image segmentation(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2018年第6期
- Page:
- 621-627
- Research Field:
- 医学放射物理
- Publishing date:
Info
- Title:
- Application of deep convolution neural network in radiotherapy planning image segmentation
- Author(s):
- DENG Jincheng1; PENG Yinglin2; LIU Changchun1; CHEN Zijie1; LEI Guosheng1; WU Jianghua1; ZHANG Guangshun2; DENG Xiaowu2
- 1. Shenzhen Yino Intelligence Techonology Co, Ltd., Shenzhen 518057, China; 2. Collaborative Innovation Center for Cancer Medicine/State Key Laboratory of Oncology in South China/Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
- Keywords:
- ; Keywords: deep learning; convolution neural network; medical image segmentation; similarity coefficient; radiotherapy
- PACS:
- R811.1
- DOI:
- DOI:10.3969/j.issn.1005-202X.2018.06.001
- Abstract:
- Objective To combine fully convolutional network (FCN) and atrous convolution (AC) for realizing automation segmentation of tissues and organs in radiotherapy planning image. Methods A total of 122 sets of chest CT images were selected in this study, in which the normal organ structures were sketched by radiotherapy physician, including 71 sets of CT images (8 532 axial slice images) as training set, 31 sets of CT images (5 559 axial slice images) as validation set, and 20 sets of CT images (3 589 axial slice images) as test set. Five kinds of published FCN models were selected and combined with AC algorithm to form 3 kinds of improved deep convolutional neural networks, namely dilation fully convolutional network (D-FCN). Training set was used for fully fine-tuning the above 8 kinds of network, and validation set was applied to validate the automatic segmentation results for obtaining the optimal model of each network, and finally, test set was used to perform segmentation test for the optimal models. The Dice similarity coefficients of automatic segmentation and physician sketching were compared for evaluating the performances of these image segmentation models. Results After being fully fine-tuned with the use of training set, each neural network model showed good performances in automatic image segmentation. The improved D-FCN 4s model achieved the best automatic segmentation results in validation test, with a global Dice of 94.38%. The Dice of automatic segmentations of left lung, right lung, pericardium, trachea and esophagus was 96.49%, 96.75%, 86.27%, 61.51% and 65.63%, respectively. Conclusion An improved D-FCN is put forward in this study and the verification test shows that the improved D-FCN can effectively improve the accuracy of automatic segmentation for radiotherapy planning image of chest, and segment multiple organs synchronously.
Last Update: 2018-06-22