Chen Cheng, Zhou Jiancang, Zhou Kangneng, Wang Zhiliang, Xiao Ruoxiu
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Department of Critical Care Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.
Diagnostics (Basel). 2021 Oct 20;11(11):1942. doi: 10.3390/diagnostics11111942.
(1) Background: COVID-19 has been global epidemic. This work aims to extract 3D infection from COVID-19 CT images; (2) Methods: Firstly, COVID-19 CT images are processed with lung region extraction and data enhancement. In this strategy, gradient changes of voxels in different directions respond to geometric characteristics. Due to the complexity of tubular tissues in lung region, they are clustered to the lung parenchyma center based on their filtered possibility. Thus, infection is improved after data enhancement. Then, deep weighted UNet is established to refining 3D infection texture, and weighted loss function is introduced. It changes cost calculation of different samples, causing target samples to dominate convergence direction. Finally, the trained network effectively extracts 3D infection from CT images by adjusting driving strategy of different samples. (3) Results: Using Accuracy, Precision, Recall and Coincidence rate, 20 subjects from a private dataset and eight subjects from Kaggle Competition COVID-19 CT dataset tested this method in hold-out validation framework. This work achieved good performance both in the private dataset (99.94-00.02%, 60.42-11.25%, 70.79-09.35% and 63.15-08.35%) and public dataset (99.73-00.12%, 77.02-06.06%, 41.23-08.61% and 52.50-08.18%). We also applied some extra indicators to test data augmentation and different models. The statistical tests have verified the significant difference of different models. (4) Conclusions: This study provides a COVID-19 infection segmentation technology, which provides an important prerequisite for the quantitative analysis of COVID-19 CT images.
(1) 背景：新型冠状病毒肺炎（COVID-19）已成为全球大流行疾病。本研究旨在从COVID-19胸部CT图像中提取三维感染区域；(2) 方法：首先，对COVID-19胸部CT图像进行肺区域提取和数据增强处理。在此策略中，不同方向体素的梯度变化反映几何特征。由于肺区域管状组织的复杂性，根据其过滤可能性将它们聚类到肺实质中心。因此，数据增强后感染区域得以改善。然后，建立深度加权U-Net来细化三维感染纹理，并引入加权损失函数。它改变了不同样本的代价计算，使目标样本主导收敛方向。最后，训练好的网络通过调整不同样本的驱动策略，有效地从CT图像中提取三维感染区域。(3) 结果：使用准确率、精确率、召回率和符合率，在留出验证框架下，对来自一个私有数据集的20名受试者和来自Kaggle竞赛COVID-19 CT数据集的8名受试者测试了该方法。本研究在私有数据集（99.94 - 100.02%、60.42 - 71.25%、70.79 - 79.35%和63.15 - 78.35%）和公共数据集（99.73 - 100.12%、77.02 - 86.06%、41.23 - 48.61%和52.50 - 68.18%）中均取得了良好的性能。我们还应用了一些额外指标来测试数据增强和不同模型。统计检验验证了不同模型之间的显著差异。(4) 结论：本研究提供了一种COVID-19感染分割技术，为COVID-19胸部CT图像的定量分析提供了重要前提条件。
