[1]张丹凤,蒋俊,吴昊天,等.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[J].中国医学物理学杂志,2023,40(12):1463-1467.[doi:DOI:10.3969/j.issn.1005-202X.2023.12.003]
　ZHANG Danfeng,JIANG Jun,WU Haotian,et al.Auto-segmentation of high-risk clinical target volume and organs-at-risk for brachytherapy of cervical cancer based on nnUNet[J].Chinese Journal of Medical Physics,2023,40(12):1463-1467.[doi:DOI:10.3969/j.issn.1005-202X.2023.12.003]
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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()

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第12期
页码:
1463-1467
栏目:
医学放射物理
出版日期:
2023-12-27

文章信息/Info

Title:
Auto-segmentation of high-risk clinical target volume and organs-at-risk for brachytherapy of cervical cancer based on nnUNet
文章编号:
1005-202X（2023）12-1463-05
作者:
张丹凤1; 蒋俊1; 吴昊天2; 裴曦2; 汪志1
1.安徽医科大学第一附属医院肿瘤放疗科， 安徽 合肥 230022； 2.安徽慧软科技有限公司， 安徽 合肥 230088
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
关键词:
宫颈癌; 深度学习; 肿瘤靶区; 自动勾画; 近距离治疗
Keywords:
Keywords: cervical cancer deep learning tumor target volume automatic segmentation brachytherapy
分类号:
R318；R815
DOI:
DOI:10.3969/j.issn.1005-202X.2023.12.003
文献标志码:
A
摘要:
目的：基于一种深度学习卷积神经网络模型（nnU-Net）实现CT引导的宫颈癌近距离治疗中高危临床靶区（HR-CTV）及危及器官（OARs）的自动勾画，探讨其临床应用价值。方法：纳入医院已完成图像引导近距离治疗的63例未手术的局部晚期宫颈癌患者的CT图像作为研究数据，由1名高年资医师对所有HR-CTV及OARs（膀胱、直肠及乙状结肠）进行统一勾画，将手动勾画的HR-CTV及OARs作为金标准，模型自动勾画结果与作为金标准的手动勾画图像进行比较。采用Dice相似性系数（DSC）对勾画的HR-CTV及OARs的精准度进行评价。结果：HR-CTV、膀胱、直肠和乙状结肠的DSC值分别为0.903±0.015、0.948±0.011、0.903±0.008及0.803±0.024。结论：本模型可准确勾画HR-CTV、膀胱、直肠及乙状结肠，但放疗医生仍应该仔细检查勾画结果。
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.

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备注/Memo

备注/Memo:
【收稿日期】2023-07-13 【基金项目】安徽省自然科学基金（1908085MA27） 【作者简介】张丹凤，硕士，研究方向：妇科近距离放射治疗，E-mail: 996399655@qq.com 【通信作者】汪志，高级工程师，研究方向：医学物理，E-mail: wang_zhi81@163.com
更新日期/Last Update: 2023-12-27