Automatic segmentation of head and neck organs at risk based on three-dimensional U-NET deep convolutional neural network.

Xiangkun Dai; Xiaoshen Wang; Lehui DU; Na MA; Shouping XU; Boning Cai; Shuxin Wang; Zhonguo Wang; Baolin QU

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Automatic segmentation of head and neck organs at risk based on three-dimensional U-NET deep convolutional neural network.

Author: Xiangkun DAI ¹ ; Xiaoshen WANG ¹ ; Lehui DU ¹ ; Na MA ¹ ; Shouping XU ¹ ; Boning CAI ¹ ; Shuxin WANG ¹ ; Zhonguo WANG ¹ ; Baolin QU ¹
Author Information

1. Department of Radiotherapy, First Medical Center of PLA General Hospital, BeiJing 100853, P.R.China.
Publication Type:Journal Article
Keywords: automatic segmentation; convolutional neural networks; deep learning; organs at risk
From: Journal of Biomedical Engineering 2020;37(1):136-141
CountryChina
Language:Chinese
Abstract: The segmentation of organs at risk is an important part of radiotherapy. The current method of manual segmentation depends on the knowledge and experience of physicians, which is very time-consuming and difficult to ensure the accuracy, consistency and repeatability. Therefore, a deep convolutional neural network (DCNN) is proposed for the automatic and accurate segmentation of head and neck organs at risk. The data of 496 patients with nasopharyngeal carcinoma were reviewed. Among them, 376 cases were randomly selected for training set, 60 cases for validation set and 60 cases for test set. Using the three-dimensional (3D) U-NET DCNN, combined with two loss functions of Dice Loss and Generalized Dice Loss, the automatic segmentation neural network model for the head and neck organs at risk was trained. The evaluation parameters are Dice similarity coefficient and Jaccard distance. The average Dice Similarity coefficient of the 19 organs at risk was 0.91, and the Jaccard distance was 0.15. The results demonstrate that 3D U-NET DCNN combined with Dice Loss function can be better applied to automatic segmentation of head and neck organs at risk.