Abstract

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Title:: Automatic segmentation of clinical target volumes and organs-at-risk in radiotherapy for cervical cancer using U-net convolutional neural network

Author(s):: QIN Nannan1; XUE Xudong2; WU Ailin2; YAN Bing2; ZHU Yadi1; ZHANG Peng2; WU Aidong1; 2; 1. School of Biomedical Engineering, Anhui Medical University, Hefei 230032, China; 2. Department of Radiation Oncology, the First Affiliated Hospital of University of Science and Technology of China, Hefei 230001, China

Keywords:: Keywords: deep learning; automatic segmentation; clinical target volume; organs-at-risk; radiotherapy; U-net

Abstract:: Abstract: Objective To explore the feasibility of deep learning based on U-net convolutional neural network for the automatic segmentation of clinical target volumes and organs-at-risk in the radiotherapy for cervical cancer. Methods U-net convolutional neural network model was used to construct an end-to-end automatic segmentation framework. The CT and tissue structure data of 100 patients with cervical cancer who had undergone intensity-modulated radiotherapy were analyzed in this study, and 10 of the patients were randomly selected as test sets. The clinical target volume, the bladder, the rectum and the left and right femoral heads were segmented. Dice similarity coefficient (DSC) and Hausdorff distance (HD) of manual and automatic segmentations were compared to evaluate the accuracy of the automatic segmentation model. Results All the DSC of organs-at-risk was above 0.833, with an average value of 0.898; and all the HD was within 8.3 mm, with an average value of 5.3 mm. The DSC and HD of clinical target volumes were 0.860 and 13.9 mm, respectively. Conclusion The automatic segmentation model established based on U-net convolutional neural network can accurately realize the automatic segmentations of clinical target volumes and organs-at-risk in the radiotherapy for cervical cancer, and it can also greatly improve the working efficiency of doctors and the consistency of segmentations in clinical application.