[1]侯东梅,赵永瑞,殷旭君,等.两种自动勾画系统勾画头部小体积危及器官的对比[J].中国医学物理学杂志,2022,39(6):676-681.[doi:DOI:10.3969/j.issn.1005-202X.2022.06.004]
　HOU Dongmei,ZHAO Yongrui,YIN Xujun,et al.Comparison of two different systems for automatic segmentation of small-sized organs-at-risk in the head[J].Chinese Journal of Medical Physics,2022,39(6):676-681.[doi:DOI:10.3969/j.issn.1005-202X.2022.06.004]
点击复制

两种自动勾画系统勾画头部小体积危及器官的对比()
分享到：

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

卷:
39卷
期数:
2022年第6期
页码:
676-681
栏目:
医学放射物理
出版日期:
2022-06-27

文章信息/Info

Title:
Comparison of two different systems for automatic segmentation of small-sized organs-at-risk in the head
文章编号:
1005-202X（2022）06-0676-06
作者:
侯东梅; 赵永瑞; 殷旭君; 张秋杭; 徐建堃
首都医科大学宣武医院放射治疗科， 北京 100053
Author(s):
HOU Dongmei;  ZHAO Yongrui;  YIN Xujun;  ZHANG Qiuhang;  XU Jiankun
Department of Radiotherapy, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
关键词:
自动勾画; 手动勾画; 危及器官; 头部肿瘤; 剂量偏差
Keywords:
Keywords: automatic segmentation manual delineation organ-at-risk head tumor dose deviation
分类号:
R811.1
DOI:
DOI:10.3969/j.issn.1005-202X.2022.06.004
文献标志码:
A
摘要:
目的：比较MANTEIA和RT-Mind两种软件自动勾画头部小体积危及器官（OAR）的准确性。方法：选取30例头部肿瘤患者的电子计算机断层扫描（CT）影像和核磁共振影像，将两套勾画系统软件自动勾画的实验组1和在勾画基础上手动修改的实验组2与医生手动勾画的对照组进行交叉指数系数（OI）、形状相似性系数（DSC）、杰卡德相似系数（J）和剂量偏差比较。结果：对于体积相对较大或CT值差异明显的头部器官（如脑干、晶体），OI、DSC、J值较高，剂量偏差较低；但对于小体积OAR（如视交叉、视神经、垂体）OI、DSC、J值较低，剂量偏差较高。P值分析发现：自动勾画A1组与手动勾画M组OAR的OI、DSC和J值比较差异均有统计学意义（P<0.05）。除晶体和内耳的OI值外，自动勾画B1组与手动勾画M组OAR的OI、DSC和J值比较差异均有统计学意义（P<0.05）。在自动勾画基础上，手动修改后，OI、DSC和J值都有提升，但与M组的OAR相比仍存在一定的差异。结论：通过软件自动勾画可以满足体积相对较大或CT值差异明显的头部器官放疗的临床需求，但对于头部小体积OAR，在临床上仍需要医生手动勾画。 【关键词】自动勾画；手动勾画；危及器官；头部肿瘤；剂量偏差
Abstract:
Abstract: Objective To compare the accuracies of MANTEIA and RT-Mind in the automatic segmentation of small-sized organs-at-risk (OAR) in the head. Methods In the computed tomography (CT) images and magnetic resonance (MR) images of 30 patients with head tumors, the OAR were auto-segmented by MANTEIA and RT-Mind (experience group 1) and further manually modified (experience group 2), and also manually delineated by a doctor (control group). The overlap index (OI), Dice similarity coefficient (DSC), Jaccard similarity coefficient (J) and dose deviation were calculated and compared between groups. Results For head OAR with relatively greater volumes (such as brainstem) or obvious differences in CT values (such as lens), the OI, DSC and J value were higher, and the dose deviations were lower. However, for small-sized OAR (such as optic chiasm, optic nerve, pituitary), the OI, DSC and J value were lower, and the dose deviations were higher. P-value analysis showed the differences in OI, DSC and J value between experience group A1 and control group were statistically significant (P<0.05). Except the OI of the lens and inner ear, there were significant differences between experience group B1 and control group in OI, DSC and J value (P<0.05). After manual modification based on auto-segmentation, the OI, DSC and J value were further improved, but there still existed discrepancy as compared with segmentation results in control group. Conclusion Automatic segmentation with the above-mentioned systems can meet the clinical needs of radiotherapy for head organs with relatively greater volumes or obvious differences in CT values. However, for small-sized OAR, manual delineation is still needed in clinical practice.

相似文献/References:

[1]张艺宝,吴昊,李莎,等.临床前验证与几何对比分析基于图谱库的危及器官自动勾画[J].中国医学物理学杂志,2015,32(06):761.[doi:doi:10.3969/j.issn.1005-202X.2015.06.001]
　[J].Chinese Journal of Medical Physics,2015,32(6):761.[doi:doi:10.3969/j.issn.1005-202X.2015.06.001]
[2]王金媛,徐寿平,杨微,等.算法和匹配数目对宫颈癌危及器官自动勾画的影响[J].中国医学物理学杂志,2019,36(11):1243.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.001]
　WANG Jinyuan,XU Shouping,YANG Wei,et al.Effects of algorithm and matching number on the auto-segmentation of organs-at-risk in patients with cervical cancer[J].Chinese Journal of Medical Physics,2019,36(6):1243.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.001]
[3]王沛沛,李金凯,李彩虹,等.基于人工智能技术的危及器官自动勾画在胸部肿瘤中的应用[J].中国医学物理学杂志,2019,36(11):1346.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.019]
　WANG Peipei,LI Jinkai,LI Caihong,et al.Application of automatic organs-at-risk segmentation based on artificial intelligence technology in thoracic tumors[J].Chinese Journal of Medical Physics,2019,36(6):1346.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.019]
[4]时飞跃,王敏,秦伟,等.智能放疗云平台自动勾画食管癌患者心脏结构的应用[J].中国医学物理学杂志,2019,36(12):1377.[doi:DOI:10.3969/j.issn.1005-202X.2019.12.003]
　SHI Feiyue,WANG Min,et al.Application of RAIC.OIS in automatic segmentation of the heart in patients with esophageal cancer[J].Chinese Journal of Medical Physics,2019,36(6):1377.[doi:DOI:10.3969/j.issn.1005-202X.2019.12.003]
[5]张富利,崔德琪,王秋生,等.基于深度学习和图谱库方法自动勾画肿瘤放疗中危及器官的比较[J].中国医学物理学杂志,2019,36(12):1486.[doi:DOI:10.3969/j.issn.1005-202X.2019.12.024]
　ZHANG Fuli,CUI Deqi,WANG Qiusheng,et al.Comparative study of deep learning- versus Atlas-based auto-segmentation of organs-at-risk in tumor radiotherapy[J].Chinese Journal of Medical Physics,2019,36(6):1486.[doi:DOI:10.3969/j.issn.1005-202X.2019.12.024]
[6]汪志,常艳奎,吴昊天,等.基于深度学习的危及器官自动勾画软件系统DeepViewer在放疗中的应用及评估[J].中国医学物理学杂志,2020,37(8):1071.[doi:DOI:10.3969/j.issn.1005-202X.2020.08.025]
　WANG Zhi,CHANG Yankui,et al.Application and evaluation of deep learning-based DeepViewer system for automatic segmentation of organs-at-risk[J].Chinese Journal of Medical Physics,2020,37(6):1071.[doi:DOI:10.3969/j.issn.1005-202X.2020.08.025]
[7]常艳奎,彭昭,周解平,等.基于U-net的心脏自动勾画模型的临床应用及改进[J].中国医学物理学杂志,2020,37(10):1218.[doi:DOI:10.3969/j.issn.1005-202X.2020.10.002]
　CHANG Yankui,PENG Zhao,ZHOU Jieping,et al.Clinical application and improvement of U-net-based model for automatic segmentation of the heart[J].Chinese Journal of Medical Physics,2020,37(6):1218.[doi:DOI:10.3969/j.issn.1005-202X.2020.10.002]
[8]陈子印,白艳春,徐巍,等.人工智能云技术在乳腺癌患者心脏亚结构自动勾画中的应用[J].中国医学物理学杂志,2020,37(12):1599.[doi:DOI:10.3969/j.issn.1005-202X.2020.12.024]
　CHEN Ziyin,BAI Yanchun,XU Wei,et al.Application of artificial intelligence cloud technology in auto-segmentation of cardiac substructure of breast cancer patients[J].Chinese Journal of Medical Physics,2020,37(6):1599.[doi:DOI:10.3969/j.issn.1005-202X.2020.12.024]
[9]时飞跃,王敏,赵紫婷,等.基于深度学习的rtStation软件自动勾画乳腺癌术后患者心脏结构的应用分析[J].中国医学物理学杂志,2021,38(6):661.[doi:DOI:10.3969/j.issn.1005-202X.2021.06.001]
　SHI Feiyue,WANG Min,et al.Application analysis of deep learning-based rtStation software in automatic delineation of the heart in patients after surgery for breast cancer[J].Chinese Journal of Medical Physics,2021,38(6):661.[doi:DOI:10.3969/j.issn.1005-202X.2021.06.001]
[10]宋威,鹿红,马珺,等.金属伪影对鼻咽癌放疗危及器官自动勾画的影响[J].中国医学物理学杂志,2021,38(10):1185.[doi:DOI:10.3969/j.issn.1005-202X.2021.10.001]
　SONG Wei,LU Hong,MA Jun,et al.Effects of metal artifacts on automatic segmentation of organs-at-risk in patients receiving radiotherapy for nasopharyngeal carcinoma[J].Chinese Journal of Medical Physics,2021,38(6):1185.[doi:DOI:10.3969/j.issn.1005-202X.2021.10.001]

备注/Memo

备注/Memo:
【收稿日期】2022-01-28 【基金项目】北京市自然科学基金（Z200022） 【作者简介】侯东梅，硕士，工程师，研究方向：放射治疗计划设计、质量控制、放射生物学、影像学，E-mail: 553761879@qq.com 【通信作者】徐建堃，副主任医师，研究方向：肿瘤精确放疗和综合治疗，E-mail: xjk_7563@163.com
更新日期/Last Update: 2022-06-27