Segmentation of Organs at Risk on Head and Neck CT for Radiotherapy Based on 3D Deep Residual Fully Convolutional Neural Network
Tian Juanxiu1,2, Liu Guocai1,4*, Gu Shanshan3, Gu Dongdong1, Gong Junhui1,2
1(College of Electrical and Information Engineering, Hunan University, Changsha 410082, China); 2(College of Computer and Communication, Hunan Institute of Engineering, Xiangtan 411104, Hunan, China); 3(Departments of Radiation Oncology, Chinese PLA General Hospital, Beijing 100853, China); 4(National Engineering Laboratory for Robot Visual Perception and Control Technology, Changsha 410082, China)
Abstract：Segmentation of organs at risk (OARs) is a crucial process during the planning of radiation therapy for head and neck cancer treatment. However, accurate OAR segmentation in CT images is a challenging task. Manual delineation of OARs is tedious, time-consuming and inconsistent. To tackle these challenges, we proposed an automatic deep-learning-based method for head and neck OARs segmentation. A modified V-Net structure was constructed to extract deep and shallow features of OARs by specialized end-to-end supervised learning. To address the extremely class imbalances of small organs, a positional prior knowledge restricted sampling strategy was proposed, and Dice loss function was used to train the network. The strategy could not only accelerate the training process and improve the segmentation performance, but also ensure the accuracy of small organ segmentation. The performance of the proposed method was validated on PDDCA dataset, which was used in Head and Neck Auto-Segmentation Challenge of MICCAI 2015. The mean Dice coefficient of each organ was 0.945 of mandible, 0.884 of left parotid gland, 0.882 of right parotid gland, 0.863 of brainstem, 0.825 of leftsubmandibular gland, 0.842 of rightsubmandibular gland, 0.807 of left optic nerve, 0.847 of right optic nerve and 0.583 of optic chiasm. The 95% of Hausdorff distances of mandible, parotid glands, brainstem and submandibular glands was all within 3 mm. The contour mean distance of all organs was less than 1.2 mm. The experimental results demonstrated that the performance of the proposed method was superior to the compared state-of-the-art algorithms on segmentation of most of OARs.
田娟秀, 刘国才, 谷珊珊, 顾冬冬, 龚军辉. 基于3D深度残差全卷积网络的头颈CT放疗危及器官自动勾画[J]. 中国生物医学工程学报, 2019, 38(3): 257-265.
Tian Juanxiu, Liu Guocai, Gu Shanshan, Gu Dongdong, Gong Junhui. Segmentation of Organs at Risk on Head and Neck CT for Radiotherapy Based on 3D Deep Residual Fully Convolutional Neural Network. Chinese Journal of Biomedical Engineering, 2019, 38(3): 257-265.
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