不同固定窗宽/窗位调节在医学图像自动分割中的应用研究

Research on the Application of Different Fixed Window Width and Window Level Adjustment in Automatic Segmentation of Medical Images

目的以肺癌放疗涉及的危及器官为例,通过对胸部CT影像进行固定窗宽(Window Width,WW)/窗位(Window Level,WL)的调节处理,探究不同WW/WL对基于深度学习的危及器官自动勾画结果的影响。方法利用2D-Unet对2017年肺癌危及器官分割比赛中的危及器官(包括左右肺、食管、脊髓和心脏)进行自动分割;训练前均进行WW/WL调节的预处理,即对训练的CT图像分别进行软组织窗、肺窗、纵隔窗、骨窗及全窗宽的调节,然后对每一种危及器官均采用相同的条件进行训练;共60例数据集,任选其中48例为训练集,余下12例为测试集;自动分割结果采用Dice相似性系数(Dice Similarity Coefficient,DSC)和95%豪斯多夫距离(Hausdorff Distance,HD)进行评估;统计方法采用Kruskal-Wallis H秩和检验或方差分析。结果不同WW/WL调节对左、右肺及脊髓的自动分割DSC值无显著影响(P=0.057、0.090、0.894);对食管和心脏的自动分割DSC值有显著影响(P<0.001)。脊髓在不同WW/WL下的95%HD值无统计学差异(P=0.116);左右肺、食管和心脏在不同WW/WL下的95%HD值均有统计学意义(P=0.005、0.001、0.007、<0.001)。结论不同的固定WW/WL调节对不同危及器官自动勾画结果的影响不同,在进行基于深度学习的CT影像自动分割时应选取合适的WW/WL。

Objective Taking organ at risk(OAR)of lung cancer as an example,through the adjustment of fixed window width(WW)and window level(WL)on CT images of thorax,to explore the influence of different WW/WL on the automatic delineation of OAR of lung cancer based on deep learning.Methods The OAR(including left and right lungs,esophagus,spinal cord and heart)in the 2017 lung cancer OAR segmentation competition were automatically segmented by 2D-Unet.WW/WL adjustment was pretreated before the training,that is,soft tissue window,lung window,mediastinal window,bone window and full window width were adjusted for the CT images,and then the same conditions were used for training for each organ at risk.There were a total of 60 datasets in the study,48 of which were training sets,and the remaining 12 were testing sets.Dice similarity coefficient(DSC)and 95%hausdorff distance(HD)were used as the evaluation criteria for the segmentation results of OAR.Statistical methods were determined by Kruskal-Wallis H rank sum test or analysis of variance.Results Different WW/WL regulation had no significant effect on the DSC values of left,right lung and spinal cord(P=0.057,0.090,0.894).The DSC values of esophagus and heart were significantly affected(P<0.001).There was no significant difference in 95%HD value of spinal cord under different WW/WL(P=0.116).95%HD values of left and right lung,esophagus and heart under different WW/WL were statistically significant(P=0.005,0.001,0.007,<0.001).Conclusion Different fixed WW/WL adjustments have different effects on different OAR,and the appropriate WW/WL should be selected for automatic segmentation of CT images based on deep learning.