Abstract

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Title:: Auto-segmentation of high-risk clinical target volume and organs-at-risk for brachytherapy of cervical cancer based on nnUNet

Author(s):: ZHANG Danfeng1; JIANG Jun1; WU Haotian2; PEI Xi2; WANG Zhi1; 1. Department of Radiation Oncology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China 2. Anhui Wisdom Technology Co., Ltd, Hefei 230088, China

Keywords:: Keywords: cervical cancer deep learning tumor target volume automatic segmentation brachytherapy

Abstract:: Abstract: Objective To develop an auto-segmentation model based on no new U-net for delineating high-risk clinical target volume (HR-CTV) and organs-at-risk (OAR) in CT-guided brachytherapy of cervical cancer, and to explore its clinical value. Methods The CT images of 63 patients with locally advanced cervical cancer who had completed image-guided brachytherapy were collected. The HR-CTV and OAR including bladder, rectum and sigmoid colon were delineated manually by a senior oncologist, and the results were taken as the gold standard. The automatic and manual segmentation results were compared, and Dice similarity coefficient was used to evaluate HR-CTV and OAR auto-segmentation accuracies. Results The Dice similarity coefficients of HR-CTV, bladder, rectum, and sigmoid colon were 0.903±0.015, 0.948±0.011, 0.903±0.008, and 0.803±0.024, respectively. Conclusion The established model can realize the accurate segmentations of HR-CTV, bladder, rectum and sigmoid colon, but the oncologist still needs to scrupulously check the results.