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Application of a multi-task learning-based light-weight convolution neural network for the automatic segmentation of organs at risk in thorax
Zhang Jie1, Yang Yiwei1, Shao Kainan1, Bai Xue1, Fang Min2, Shan Guoping1, Chen Ming2
1Department of Radiation Physics, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310022,China; 2Department of Radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou 310022,China
AbstractObjective To evaluate the application of a multi-task learning-based light-weight convolution neural network (MTLW-CNN) for the automatic segmentation of organs at risk (OARs) in thorax. Methods MTLW-CNN consisted of several layers for sharing features and 3 branches for segmenting 3 OARs. 497 cases with thoracic tumors were collected. Among them, the computed tomography (CT) images encompassing lung, heart and spinal cord were included in this study. The corresponding contours delineated by experienced radiation oncologists were ground truth. All cases were randomly categorized into the training and validation set (n=300) and test set (n=197). By applying MTLW-CNN on the test set, the Dice similarity coefficients (DSCs) of 3 OARs,training and testing time and space complexity (S)were calculated and compared with those of Unet and DeepLabv3+. To evaluate the effect of multi-task learning on the generalization performance of the model, 3 single-task light-weight CNNs (STLW-CNNs) were built. Their structures were totally the same as the corresponding branches in MTLW-CNN. After using the same data and algorithm to train STLW-CNN, the DSCs were statistically compared with MTLW-CNN on the testing set. Results For MTLW-CNN, the averages (μ) of lung, heart and spinal cord DSCs were 0.954, 0.921 and 0.904, respectively. The differences of μ between MTLW-CNN and other two models (Unet and DeepLabv3+) were less than 0.020. The training and testing time of MTLW-CNN were 1/3 to 1/30 of that of Unet and DeepLabv3+. S of MTLW-CNN was 1/42 of that of Unet and 1/1220 of that of DeepLabv3+. The differences of μ and standard deviation (σ) of lung and heart between MTLW-CNN and STLW-CNN were approximately 0.005 and 0.002. The difference of μ of spinal cord was 0.001, but σ of STLW-CNN was 0.014higher than that of MTLW-CNN. Conclusions MTLW-CNN spends less time and space on high-precision automatic segmentation of thoracic OARs. It can improve the application efficiency and generalization performance of the models.
Fund:Zhejiang Key Research & Development Project (2019C03003);Natural Science Foundation of Zhejiang Province (LQ20H180016);Zhejiang Medical and Health Project (2019RC023,2019ZH018)
Zhang Jie,Yang Yiwei,Shao Kainan et al. Application of a multi-task learning-based light-weight convolution neural network for the automatic segmentation of organs at risk in thorax[J]. Chinese Journal of Radiation Oncology, 2021, 30(9): 917-923.
Zhang Jie,Yang Yiwei,Shao Kainan et al. Application of a multi-task learning-based light-weight convolution neural network for the automatic segmentation of organs at risk in thorax[J]. Chinese Journal of Radiation Oncology, 2021, 30(9): 917-923.
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2021, Vol. 30 
