Zhongjian Ju1, Qingnan Wu2, Wei Yang1, Shanshan Gu1, Wen Guo3, Jinyuan Wang1, Ruigang Ge1, Hong Quan3, Jie Liu4, Baolin Qu1. 1. Department of Radiation Oncology, The First Medical Center of People's Liberation Army General Hospital, Beijing, China. 2. Department of Radiation Therapy, Peking University International Hospital, Beijing, China. 3. School of Physics Science and Technology, Wuhan University, Wuhan, China. 4. Beijing Eastraycloud Technology Inc, Beijing, China.
Abstract
Background: Efficient and accurate methods are needed to automatically segmenting organs-at-risk (OAR) to accelerate the radiotherapy workflow and decrease the treatment wait time. We developed and evaluated the use of a fused model Dense V-Network for its ability to accurately segment pelvic OAR.Material and methods: We combined two network models, Dense Net and V-Net, to establish the Dense V-Network algorithm. For the training model, we adopted 100 kV computed tomography (CT) images of patients with cervical cancer, including 80 randomly selected as training sets, by which to adjust parameters of the automatic segmentation model, and the remaining 20 as test sets to evaluate the performance of the convolutional neural network model. Three representative parameters were used to evaluate the segmentation results quantitatively. Results: Clinical results revealed that Dice similarity coefficient values of the bladder, small intestine, rectum, femoral head and spinal cord were all above 0.87 mm; and Jaccard distance was within 2.3 mm. Except for the small intestine, the Hausdorff distance of other organs was less than 9.0 mm. Comparison of our approaches with those of the Atlas and other studies demonstrated that the Dense V-Network had more accurate and efficient performance and faster speed.Conclusions: The Dense V-Network algorithm can be used to automatically segment pelvic OARs accurately and efficiently, while shortening patients' waiting time and accelerating radiotherapy workflow.
Background: Efficient and accurate methods are needed to automatically segmenting organs-at-risk (OAR) to accelerate the radiotherapy workflow and decrease the treatment wait time. We developed and evaluated the use of a fused model Dense V-Network for its ability to accurately segment pelvic OAR.Material and methods: We combined two network models, Dense Net and V-Net, to establish the Dense V-Network algorithm. For the training model, we adopted 100 kV computed tomography (CT) images of patients with cervical cancer, including 80 randomly selected as training sets, by which to adjust parameters of the automatic segmentation model, and the remaining 20 as test sets to evaluate the performance of the convolutional neural network model. Three representative parameters were used to evaluate the segmentation results quantitatively. Results: Clinical results revealed that Dice similarity coefficient values of the bladder, small intestine, rectum, femoral head and spinal cord were all above 0.87 mm; and Jaccard distance was within 2.3 mm. Except for the small intestine, the Hausdorff distance of other organs was less than 9.0 mm. Comparison of our approaches with those of the Atlas and other studies demonstrated that the Dense V-Network had more accurate and efficient performance and faster speed.Conclusions: The Dense V-Network algorithm can be used to automatically segment pelvic OARs accurately and efficiently, while shortening patients' waiting time and accelerating radiotherapy workflow.