Meng X L, Xing Z J, Lu S
Tianjin Medical University Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Radiology Department TianJin 300134.
Deepwise AI Lab, Deepwise Inc., Beijing, 100080.
Zhonghua Yi Xue Za Zhi. 2021 Feb 23;101(7):476-480. doi: 10.3760/cma.j.cn112137-20201123-03171.
To evaluate the diagnostic value of the lung nodule classification and segmentation algorithm based on deep learning among different CT reconstruction algorithms. Chest CT of 363 patients from June 2019 to September 2019 in Radiology Department of Tianjin Medical University Chu Hsien-I Memorial Hospital were retrospectively collected in this study, each of which consisted of images by three different reconstruction methods (lung reconstruction, mediastinal reconstruction, bone reconstruction).These collected data were used as testing set and a total of 4 185 Chest CTs including the public data set and the constructed private data set were used as the training set. A model combines 3D deep convolutional neural network and recurrent neural network under a multi-task joint learning algorithm for lung nodule classification and segmentation were constructed. The well-trained method was tested on 363 test cases using two metrics, i.e., the accuracy of the density classification and the Dice coefficient of nodule segmentation. The performances under three reconstruction methods were statistically analyzed according to the variance analysis among three different reconstruction methods. The average classification accuracies of the nodule under three reconstruction methods were 98.67%±5.70%, 98.38%±6.61% and 97.89%±7.32%. Specifically, the accuracies of the solid nodules under three reconstruction methods were 98.79%±5.58%, 98.49%±6.89% and 97.90%±7.41% and the accuracies of the sub-solid nodules were 97.57%±10.19%, 98.52%±7.77% and 98.52%±7.77%. There was no significant difference in the classification accuracy of pulmonary nodules under three different reconstruction algorithms (all >0.05). The average Dice coefficients of nodule segmentation was 79.87%±5.78%, 79.02%±6.04% and 79.31%±5.95%. There was no significant difference in the average Dice coefficients of nodule segmentation under three different reconstruction algorithms (all >0.05). Deep learning algorithm which combined with 3D convolutional neural network and recurrent neural network has demonstrated relatively stable in classification and segmentation of lung nodules under different CT reconstruction method.
评估基于深度学习的肺结节分类与分割算法在不同CT重建算法中的诊断价值。本研究回顾性收集了2019年6月至2019年9月天津医科大学朱宪彝纪念医院放射科363例患者的胸部CT，每例均包含三种不同重建方法（肺重建、纵隔重建、骨重建）的图像。这些收集的数据用作测试集，共4185例胸部CT（包括公共数据集和构建的私有数据集）用作训练集。构建了一种在多任务联合学习算法下结合3D深度卷积神经网络和循环神经网络的肺结节分类与分割模型。使用密度分类准确率和结节分割的Dice系数这两个指标，在363个测试病例上对训练良好的方法进行测试。根据三种不同重建方法之间的方差分析，对三种重建方法下的性能进行统计学分析。三种重建方法下结节的平均分类准确率分别为98.67%±5.70%、98.38%±6.61%和97.89%±7.32%。具体而言，三种重建方法下实性结节的准确率分别为98.79%±5.58%、98.49%±6.89%和97.90%±7.41%，亚实性结节的准确率分别为97.57%±10.19%、98.52%±7.77%和98.52%±7.77%。三种不同重建算法下肺结节的分类准确率无显著差异（均>0.05）。结节分割的平均Dice系数分别为79.87%±5.78%、79.02%±6.04%和79.31%±5.95%。三种不同重建算法下结节分割的平均Dice系数无显著差异（均>0.05）。结合3D卷积神经网络和循环神经网络的深度学习算法在不同CT重建方法下对肺结节的分类和分割表现出相对稳定的性能。
