[1]胡兴刚,王娴,张扬,等.基于深度学习算法的自动勾画系统在头颈部危及器官勾画精度的研究[J].中国医学物理学杂志,2024,41(5):548-553.[doi:DOI:10.3969/j.issn.1005-202X.2024.05.004]
　HU Xinggang,WANG Xian,ZHANG Yang,et al.Deep learning based software solutions for automatic segmentation of head and neck organs at risk[J].Chinese Journal of Medical Physics,2024,41(5):548-553.[doi:DOI:10.3969/j.issn.1005-202X.2024.05.004]
点击复制

基于深度学习算法的自动勾画系统在头颈部危及器官勾画精度的研究()
分享到：

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

卷:
41卷
期数:
2024年第5期
页码:
548-553
栏目:
医学放射物理
出版日期:
2024-05-23

文章信息/Info

Title:
Deep learning based software solutions for automatic segmentation of head and neck organs at risk
文章编号:
1005-202X（2024）05-0548-06
作者:
胡兴刚1; 王娴1; 张扬1; 张玉雷2; 李校宣1; 陈猛1
1.普洱市人民医院肿瘤中心， 云南 普洱 665000； 2.洛阳市中心医院放疗科， 河南 洛阳 471000
Author(s):
HU Xinggang1;  WANG Xian1;  ZHANG Yang1;  ZHANG Yulei2;  LI Xiaoxuan1;  CHEN Meng1
1. Cancer Center, Puer Peoples Hospital, Puer 665000, China 2. Department of Radiology, Luoyang Central Hospital, Luoyang 471000, China
关键词:
自动勾画; 头颈部危及器官; 深度学习
Keywords:
Keywords: automatic segmentation head and neck organs at risk deep learning
分类号:
R318；R811.1
DOI:
DOI:10.3969/j.issn.1005-202X.2024.05.004
文献标志码:
A
摘要:
目的：评估和分析3种基于深度学习技术的自动勾画系统在勾画头颈部危及器官（OAR）中的准确度。方法：以放疗医师手工勾画的OAR为标准，通过体积相似性系数（DSC）、豪斯多夫距离（HD）、感兴趣区域质心偏差（COMD）、过分割率（FNR）、欠分割率（FPR）、Jaccard系数（JC）、灵敏度指数（SI）及包容性系数（II）等参数评估PV-iCurve、RT-Mind和AccuContour自动勾画系统在头颈部OAR勾画的精度。结果：脑的FNR、JC、SI值，脑干的FPR、II值，左眼的FPR、FNR、JC、SI值，下颌骨的FPR、FNR、SI、II值，左腮腺的FPR、FNR、SI、II值及脊髓的DSC、FPR、JC、II值均显示3种勾画系统间存在统计学差异（P<0.05），只有脑干的HD、FNR、SI值和脊髓的HD值显示3种自动勾画系统勾画结果无统计学差异（P>0.05）。结论：通过多个参数的比较，发现3种软件在不同OAR勾画中的勾画精度不同，难以进行整体横向比较，因此这些参数仅作为参考，不能用于评估勾画结果作为临床治疗的标准，虽然3种软件都有较好的勾画结果，但仍需医师仔细审核和做必要的修改。
Abstract:
Abstract: Objective To evaluate and analyze the accuracies of 3 software solutions based on deep learning techniques in the automatic segmentation of head and neck organs at risk (OAR). Methods The automatic segmentation accuracies of 3 software (PV-iCurve, RT-Mind, and AccuContour) were evaluated with Dice similarity coefficient (DSC), Hausdorff distance (HD), center of mass deviation (COMD), false negative rate (FNR), false positive rate (FPR), Jaccard coefficient (JC), sensitivity index (SI), and inclusive index (II) using the manual contours of head and neck OAR as the gold standard. Results The FNR, JC, SI of brain, the FPR, II of brainstem, the FPR, FNR, JC, SI of eye_L, the FPR, FNR, SI, II of mandible, the FPR, FNR, SI, II of parotid_L, and the DSC, FPR, JC, II of spinal cord manifested significant differences among the 3 software (P<0.05) but the HD, FNR, SI of brainstem, and the HD of spinal cord revealed trivial differences among the 3 software (P>0.05). Conclusion Through the comparison of multiple parameters, it is found that the accuracies of 3 software are different in OAR segmentation, which makes it difficult to make overall horizontal comparisons. Therefore, these parameters are for reference only and cannot be used as criteria for evaluating the segmentation results in clinic. Although all 3 software achieve preferable segmentation outcomes, scrutiny and manual modifications before clinical practice are still necessary.

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(5):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(5):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(5):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(5):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(5):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(5):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(5):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(5):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(5):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(5):1185.[doi:DOI:10.3969/j.issn.1005-202X.2021.10.001]

备注/Memo

备注/Memo:
【收稿日期】2023-10-26 【基金项目】普洱市人民医院院内项目（2021YN01） 【作者简介】胡兴刚，硕士，物理师，研究方向：剂量验证，E-mail: hxg163453@163.com 【通信作者】王娴，副主任医师，研究方向：肿瘤学，E-mail: ynpezlk@163.com
更新日期/Last Update: 2024-05-24