COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancing.
COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancing.
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DOI：
10.1016/j.imu.2021.100709
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发表时间：
2021
影响因子：
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通讯作者：
Moni MA
中科院分区：
文献类型：
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作者：
Hasan MK;Jawad MT;Hasan KNI;Partha SB;Masba MMA;Saha S;Moni MA
新奇的新冠肺炎是一种在全球范围内蔓延的全球性流行病。具有更高灵敏度的计算机辅助筛查工具对于疾病的早期诊断和预后是必不可少的。它也可以成为新冠肺炎患者分诊测试和临床监督的有用工具。然而，从非侵入性放射图像设计这样的自动化工具是具有挑战性的，因为许多人工标注的数据集还不是公开可用的，这是监督学习方案的基本核心要求。本文提出了一种基于三维卷积神经网络(CNN)的分类方法，该方法同时考虑了切片间和切片内的空间体素信息。该系统端到端地对整个体积CT(CT)图像中的3D块进行训练，以扩大训练样本的数量，执行关于块大小确定的消融研究。我们将渐进式调整大小、细分、扩展和类别重新平衡集成到我们的3D网络中。分割是新冠肺炎诊断的关键前提步骤，使分类器能够学习突出的肺部特征，同时排除CT扫描的肺外区域。我们在一个名为MosMed的公开可用的数据集上评估了所有广泛的实验，该数据集具有二进制和多类胸部CT图像分区。我们的实验结果非常令人鼓舞，分别适用于二类和多类任务的接收器操作特征(ROC)曲线下的产出区，应用了5次交叉验证。我们的方法有希望的结果表明，它是临床医生和放射科医生评估新冠肺炎的有利辅助工具。
The novel COVID-19 is a global pandemic disease overgrowing worldwide. Computer-aided screening tools with greater sensitivity are 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 end-to-end on the 3D patches from the whole volumetric Computed Tomography (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 into 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 Receiver Operating Characteristics (ROC) curve of and 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.