Abstract

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Title:: Application and evaluation of deep learning-based DeepViewer system for automatic segmentation of organs-at-risk

Author(s):: WANG Zhi 1; 2; CHANG Yankui1; WU Haotian 3; ZHANG Jian 3; XU Xie1; PEI Xi 1; 3; 1. Center of Radiological Medical Physics, University of Science and Technology of China, Hefei 230025, China 2. Department of Radiation Oncology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China 3. Anhui Wisdom Technology Co., Ltd., Hefei 230088, China

Keywords:: Keywords: deep learning organ-at-risk automatic segmentation radiotherapy

Abstract:: Abstract: Objective To realize the automatic segmentation of organs-at-risk (OAR) by DeepViewer system which was an automatic OAR segmentation software based on deep learning. Methods The OAR in CT images was automatically segmented by DeepViewer system via improved U-Net convolutional neural network. Dice similarity coefficient (DSC) was used to compare the differences between the automatic segmentations and manual segmentations of 22 OAR. Results There were 11 OAR with an average DSC above 0.9, 5 OAR with an average DSC of 0.8-0.9, and 5 OAR with an average DSC of 0.7-0.8. The mean DSC of optic chiasma was the lowest (0.676). The overall results showed that DeepViewer system could be used to realize the automatic segmentation of OAR, especially left lung, right lung, bladder, brain stem and so on, which basically met the clinical requirements. Conclusion DeepViewer system can be used to automatically segment OAR in tumor patients, with a high accuracy. Meanwhile, RTStructure DICOM3.0 files can be transmitted automatically through the network after the segmentation by DeepViewer, thus greatly facilitating the workflow of clinicians and shortening the total segmentation time in treatment planning.