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

Deep learning based software solutions for automatic segmentation of head and neck organs at risk

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。

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.