Auto-segmentation of high-risk clinical target volume and organs-at-risk for brachytherapy of cervical cancer based on nnUNet
10.3969/j.issn.1005-202X.2023.12.003
- VernacularTitle:基于nnU-Net的宫颈癌近距离治疗中高危临床靶区及危及器官的自动勾画
- Author:
Danfeng ZHANG
1
;
Jun JIANG
;
Haotian WU
;
Xi PEI
;
Zhi WANG
Author Information
1. 安徽医科大学第一附属医院肿瘤放疗科,安徽合肥 230022
- Keywords:
cervical cancer;
deep learning;
tumor target volume;
automatic segmentation;
brachytherapy
- From:Chinese Journal of Medical Physics
2023;40(12):1463-1467
- CountryChina
- Language:Chinese
-
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.
