Xin Yang1, Xueyan Li1,2, Xiaoting Zhang1,2, Fan Song1,3, Sijuan Huang1, Yunfei Xia1. 1. Sun Yat- sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China. 2. Xinhua College of Sun Yat-sen University, Guangzhou 510520, China. 3. Guangdong University of Technology, Guangzhou 510006, China.
Abstract
OBJECTIVE: To investigate the accuracy of automatic segmentation of organs at risk (OARs) in radiotherapy for nasopharyngeal carcinoma (NPC). METHODS: The CT image data of 147 NPC patients with manual segmentation of the OARs were randomized into the training set (115 cases), validation set (12 cases), and the test set (20 cases). An improved network based on three-dimensional (3D) Unet was established (named as AUnet) and its efficiency was improved through end-to-end training. Organ size was introduced as a priori knowledge to improve the performance of the model in convolution kernel size design, which enabled the network to better extract the features of different organs of different sizes. The adaptive histogram equalization algorithm was used to preprocess the input CT images to facilitate contour recognition. The similarity evaluation indexes, including Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD), were calculated to verify the validity of segmentation. RESULTS: DSC and HD of the test dataset were 0.86±0.02 and 4.0±2.0 mm, respectively. No significant difference was found between the results of AUnet and manual segmentation of the OARs (P > 0.05) except for the optic nerves and the optic chiasm. CONCLUSIONS: AUnet, an improved deep learning neural network, is capable of automatic segmentation of the OARs in radiotherapy for NPC based on CT images, and for most organs, the results are comparable to those of manual segmentation.
OBJECTIVE: To investigate the accuracy of automatic segmentation of organs at risk (OARs) in radiotherapy for nasopharyngeal carcinoma (NPC). METHODS: The CT image data of 147 NPC patients with manual segmentation of the OARs were randomized into the training set (115 cases), validation set (12 cases), and the test set (20 cases). An improved network based on three-dimensional (3D) Unet was established (named as AUnet) and its efficiency was improved through end-to-end training. Organ size was introduced as a priori knowledge to improve the performance of the model in convolution kernel size design, which enabled the network to better extract the features of different organs of different sizes. The adaptive histogram equalization algorithm was used to preprocess the input CT images to facilitate contour recognition. The similarity evaluation indexes, including Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD), were calculated to verify the validity of segmentation. RESULTS: DSC and HD of the test dataset were 0.86±0.02 and 4.0±2.0 mm, respectively. No significant difference was found between the results of AUnet and manual segmentation of the OARs (P > 0.05) except for the optic nerves and the optic chiasm. CONCLUSIONS: AUnet, an improved deep learning neural network, is capable of automatic segmentation of the OARs in radiotherapy for NPC based on CT images, and for most organs, the results are comparable to those of manual segmentation.
Entities:
Keywords:
AUnet; CT images; auto segmentation; deep learning; improved Unet architecture
Authors: Andre Esteva; Brett Kuprel; Roberto A Novoa; Justin Ko; Susan M Swetter; Helen M Blau; Sebastian Thrun Journal: Nature Date: 2017-01-25 Impact factor: 49.962
Authors: Kuo Men; Huaizhi Geng; Chingyun Cheng; Haoyu Zhong; Mi Huang; Yong Fan; John P Plastaras; Alexander Lin; Ying Xiao Journal: Med Phys Date: 2018-12-07 Impact factor: 4.071
Authors: Richard Sims; Aurelie Isambert; Vincent Grégoire; François Bidault; Lydia Fresco; John Sage; John Mills; Jean Bourhis; Dimitri Lefkopoulos; Olivier Commowick; Mehdi Benkebil; Grégoire Malandain Journal: Radiother Oncol Date: 2009-09-14 Impact factor: 6.280
Authors: Wai Tong Ng; Barton But; Horace C W Choi; Remco de Bree; Anne W M Lee; Victor H F Lee; Fernando López; Antti A Mäkitie; Juan P Rodrigo; Nabil F Saba; Raymond K Y Tsang; Alfio Ferlito Journal: Cancer Manag Res Date: 2022-01-26 Impact factor: 3.989