Abstract

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Title:: Comparison of target segmentation results of two auto-segmentation methods for T3 nasopharyngeal carcinoma

Author(s):: GUO Yi1; 2; 3; LAN Linzhen1; 2; CHEN Qingquan4; LIU Xuanyu1; 2; CHEN Jun1; 2; CHEN Shuying1; 2; GUO Feibao1; 2; 3; 1. Department of Radiation Therapy, Cancer Center, the First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China 2. National Regional Medical Center, Binhai Branch of the First Affiliated Hospital of Fujian Medical University, Fuzhou 350212, China 3. Key Laboratory of Radiation Biology of Fujian Higher Education Institutions, Fuzhou 350005, China 4. Datu Medical Technology Co., Ltd., Shanghai 200062, China

Keywords:: Keywords: nasopharyngeal carcinoma intensity modulated radiation therapy deep learning target segmentation

Abstract:: Abstract: Objective To compare and analyze the accuracy of tumor segmentation using Atlas-based auto-segmentation method and deep learning method for T3 nasopharyngeal carcinoma (NPC). Methods A total of 138 cases of T3 NPC were selected retrospectively. GTV and CTV were outlined in CT by one senior doctor, and the delineation results were reviewed by two other senior doctors. A 3D-Unet model was established, with 110 cases as the training set and 28 cases as the test set. The performances of 3D-Unet model and Atlas models were compared. Results The ASD, 95%HD, DSC, JSC, precision, and recall rate of GTV and CTV segmentations using 3D-Unet model were 3.01 and 1.84 mm, 16.05 and 7.70 mm, 0.71 and 0.83, 0.56 and 0.71, 0.70 and 0.85, 0.76 and 0.81, respectively. The comparison of the above mentioned parameters revealed that 3D-Unet model was superior to Atlas models (P<0.05). Conclusion Compared with Atlas models, the 3D-Unet based auto-segmentation method significantly improves the accuracy of target segmentation in NPC.