Automatic statistical diagnosis of COVID-19 based on multi-modal CT feature extraction

Xiaohong Fan , Zhichao Zuo , Yunhua Li , Yingjun Zhou , Haibo Liu , Xiao Zhou , Jianping Zhang
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Abstract

Background and purpose

Computed tomography (CT) is highly sensitive to lung-related abnormalities as a non-invasive method and has become an essential tool for screening and diagnosing Coronavirus disease 2019 (COVID-19). To reduce the stress of work for physicians and speed up diagnosis, we propose a novel automatic diagnosis pipeline for COVID-19 based on high-dimensional radiomic features extracted from multimodal CT scans (multi-geometric and multiscale).

Materials and methods

There are 746 CT scans involved in this study, where 349 CT scans are COVID-19 positive and 397 CT scans are COVID-19 negative. All of them are from the public dataset. We first construct a transfer learning-based auto-segmentation model with a morphological post-processing block to improve the lung region segmentation. Then the radiomics feature extraction is guided by the proposed multi-modal CT scans strategy. In addition, our automatic diagnosis pipeline is driven by a well-designed loss function. We also explain the diagnosis capability from the related theory of linear subspace spanned by multi-modal radiomics features.

Results

Under the 10-fold cross-validation strategy, our approach can achieve an improvement in diagnostic performance of 5. 77%, 7. 78%, 7. 74%, 7. 78%, 7. 45% compared to the radiomic features extracted from the original CT scans, and diagnosis performance is promoted to 91.53%, 86.46%, 86.47%, 86.46%, 86.95% in terms of AUC, Acc, F1, Recall and Precision in public datasets.

Conclusions

We demonstrate a statistically significant improvement of the proposed statistical learning method compared to the state-of-the-art machine learning-based diagnosis approaches. Thanks to theoretical support and excellent diagnostic performance, our method can be deployed in clinical auxiliary diagnosis, releasing the overstretched medical resources.

基于多模态CT特征提取的COVID-19自动统计诊断
背景和目的计算机断层扫描(CT)作为一种非侵入性方法,对肺部相关异常高度敏感,已成为筛查和诊断2019冠状病毒病(新冠肺炎)的重要工具。为了减轻医生的工作压力并加快诊断,我们提出了一种新的新冠肺炎自动诊断管道,该管道基于从多模式CT扫描(多几何和多尺度)中提取的高维放射学特征。材料与方法本研究共有746例CT扫描,其中349例为新冠肺炎阳性,397例为新冠肺炎阴性。所有这些都来自公共数据集。我们首先构建了一个基于迁移学习的自动分割模型,该模型带有形态学后处理块,以改进肺部区域的分割。然后以所提出的多模式CT扫描策略为指导进行放射组学特征提取。此外,我们的自动诊断管道是由精心设计的损失函数驱动的。我们还从多模态放射组学特征所跨越的线性子空间的相关理论中解释了诊断能力。结果在10倍交叉验证策略下,我们的方法可以实现5的诊断性能改进。77%,7。78%,7。74%,7。78%,7。与从原始CT扫描中提取的放射学特征相比,诊断性能提高到91.53%、86.46%、86.47%、86.46%和86.95%。结论与最先进的基于机器学习的诊断方法相比,我们证明了所提出的统计学习方法在统计学上的显著改进。由于理论支持和出色的诊断性能,我们的方法可以应用于临床辅助诊断,释放了过度紧张的医疗资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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