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基于卷积神经网络的直肠癌靶区及危及器官自动勾画
夏祥, 王佳舟, 杨立峰, 章真, 胡伟刚
复旦大学附属肿瘤医院放射治疗科 复旦大学上海医学院肿瘤学系 200032
Automatic delineation of rectal cancer target volume and organs at risk based on convolutional neural network
Xia Xiang, Wang Jiazhou, Yang Lifeng, Zhang Zhen, Hu Weigang
Department of Radiotherapy, Cancer Hospital, Fudan University;Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
Abstract：Objective To realize automatic delineation of rectal cancer target volume and normal tissues and improve clinical work efficiency. Methods The deep learning method based on convolutional neural network was adopted to construct neural network, learn and realize automatic delineation, and compare the differences between automatic delineation and manual delineation. Results Two hundred and ten caseswith rectal cancer were randomly assigned to a training set of 190 and a validation set of 20. The complete delineation of a single case took about 10s;the average Dice of CTV was 0.87±0.04;the average Dice of other normal tissues was bigger than 0.8;the Hausdorff distance (HD) index of CTV was 25.33±16.05;the mean distance to agreement (MDA) index was 3.07±1.49, and the Jaccard similarity coefficient (JSC) index was 0.77±0.07. Conclusion The deep learning method based on full convolutional neural network can realize the automatic delineation of rectal cancer target volume and improve work efficiency.
Xia Xiang,Wang Jiazhou,Yang Lifeng et al. Automatic delineation of rectal cancer target volume and organs at risk based on convolutional neural network[J]. Chinese Journal of Radiation Oncology, 2020, 29(5): 374-368.
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