Deep learning based software solutions for automatic segmentation of head and neck organs at risk
10.3969/j.issn.1005-202X.2024.05.004
- VernacularTitle: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
- Author:
Xinggang HU
1
;
Xian WANG
;
Yang ZHANG
;
Yulei ZHANG
;
Xiaoxuan LI
;
Meng CHEN
Author Information
1. 普洱市人民医院肿瘤中心,云南普洱 665000
- Keywords:
automatic segmentation;
head and neck organs at risk;
deep learning
- From:
Chinese Journal of Medical Physics
2024;41(5):548-553
- CountryChina
- Language:Chinese
-
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.
