使用逐步调整大小的3D- CNN结合分割、增强和类别重新平衡从容积胸部CT扫描中识别COVID-19
COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancing
摘要：
新型COVID-19是一种全球性大流行性疾病，在全球范围内蔓延。具有更高灵敏度的计算机辅助筛查工具对于尽早进行疾病诊断和预后至关重要。它也可以成为COVID-19患者检测和临床监督的分诊有用工具。然而，从非侵入性射线照相图像设计这样的自动化工具是具有挑战性的，因为许多手动注释的数据集尚未公开，这是监督学习方案的基本核心要求。本文提出了一种基于3D卷积神经网络（CNN）的分类方法，同时考虑切片间和切片内的空间体素信息。所提出的系统在来自整个体积CT图像的3D贴片上以端到端的方式进行训练，以扩大训练样本的数量，对贴片尺寸确定进行消融研究。我们将渐进式优化、分割、增强和类别重新平衡集成到我们的3D网络中。分割是COVID-19诊断的关键先决步骤，使分类器能够学习突出的肺部特征，同时排除CT扫描的外部肺部区域。我们评估了所有广泛的实验上公开可用的数据集，命名为MosMed，具有二进制和多类胸部CT图像分区。我们的实验结果是非常令人鼓舞的，产生的ROC曲线下面积为0.914和0.893的二进制和多类任务，分别应用5倍交叉验证。我们的方法的有希望的结果代表它作为一个有利的辅助工具，为临床医生和放射科医生评估COVID-19。
英文摘要：
The novel COVID-19 is a global pandemic disease overgrowing worldwide. Computer-aided screening tools with greater sensitivity is imperative for disease diagnosis and prognosis as early as possible. It also can be a helpful tool in triage for testing and clinical supervision of COVID-19 patients. However, designing such an automated tool from non-invasive radiographic images is challenging as many manually annotated datasets are not publicly available yet, which is the essential core requirement of supervised learning schemes. This article proposes a 3D Convolutional Neural Network (CNN)-based classification approach considering both the inter- and intra-slice spatial voxel information. The proposed system is trained in an end-to-end manner on the 3D patches from the whole volumetric CT images to enlarge the number of training samples, performing the ablation studies on patch size determination. We integrate progressive resizing, segmentation, augmentations, and class-rebalancing to our 3D network. The segmentation is a critical prerequisite step for COVID-19 diagnosis enabling the classifier to learn prominent lung features while excluding the outer lung regions of the CT scans. We evaluate all the extensive experiments on a publicly available dataset, named MosMed, having binary- and multi-class chest CT image partitions. Our experimental results are very encouraging, yielding areas under the ROC curve of 0.914 and 0.893 for the binary- and multi-class tasks, respectively, applying 5-fold cross-validations. Our method's promising results delegate it as a favorable aiding tool for clinical practitioners and radiologists to assess COVID-19.
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