基于RNNU-Net深度学习模型的肺癌CT图像心脏自动勾画研究及临床可行性评估OACSTPCD

Objective:This study aims to assess the accuracy and clinical feasibility of automatic heart segmentation on CT images of lung cancer using the RNNU-Net deep learning model.Methods:CT images from 75 lung cancer patients was collected.Heart label maps were manually delineated by experts to create the dataset.The dataset was randomly classified into a training set(n=51),a validation set(n=7)and a test set(n=17).Data augmentation techniques were applied to expand the training set.Quantitative evaluation metrics including Dice similarity coefficient(DSC),Jaccard similarity coeffi-cient(JSC),positive predictive value(PPV),sensitivity(SE),Hausdorff distance(HD),relative volume difference(RVD),and volume overlap error(VOE)were used to assess the model.The heart segmentation results of the U-Net model were compared to those of junior practitioners.Results:the evaluation indices of the test set using the RNNU-Net deep learning model were as follows:DSC(91.06％±10.94％),JSC(85.09％±15.10％),PPV(96.01％±9.35％),SE(88.21％±13.42％),HD(4.66±1.26),RVD(12.45％±18.70％)and VOE(13.48±20.11).Statistical analysis revealed significant differences between the evaluation indices of the RNNU-Net model and those of junior doctors,with the RNNU-Net model demonstrating superior performance.Scatter plot and box plot results indicated that the RNNU-Net model had fewer zero values compared to the junior doctors.Qualitative evaluation demonstrated that the RNNU-Net deep learning model accurately segmented the inferior vena cava and the heart,resulting in smoother boundaries and decreasing missed segmentation.Furthermore,the RNNU-Net model effectively reduced over-segmentation in cases of invasion around the heart,compared to the U-Net model.Conclusion:The deep learning mod-el based on RNNU-Net exhibits advantages in the automatic delineation of the heart on CT images of lung cancer.It reduces the time required for clinical delineation and effectively compensates for missed delineation.