肿瘤预防与治疗2024，Vol.37Issue(11)：960-969,10.DOI:10.3969/j.issn.1674-0904.2024.11.006
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
Study and Clinical Feasibility Evaluation of Automatic Heart Segmentation in Lung Cancer CT Images Based on RNNU-Net Deep Learning Model
许亚萍 1孙历 2张孝文 1修玉涛 2文晓博 2崔文举 1刘泉源1
作者信息
- 1. 256500 山东滨州,滨州医学院附属医院放射科
- 2. 266071 山东青岛,青岛大学青岛肿瘤研究院
- 折叠
摘要
Abstract
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.关键词
深度学习/心脏/自动勾画/U-NetKey words
Deep learning/Heart/Auto-segmentation/U-Net分类
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许亚萍,孙历,张孝文,修玉涛,文晓博,崔文举,刘泉源..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[J].肿瘤预防与治疗,2024,37(11):960-969,10.
