Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images.
Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images.
复制标题
使用CT扫描图像自动分割和测量COVID-19肺部感染区域。
DOI：
10.1016/j.patcog.2020.107747
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发表时间：
2021-06
影响因子：
8
通讯作者：
Kassah Laouar A
中科院分区：
文献类型：
--
作者：
Oulefki A;Agaian S;Trongtirakul T;Kassah Laouar A
历史表明，传染病（COVID-19）可以迅速震惊世界，对健康造成巨大损失，从安全和经济角度对数十亿人的生活产生深远影响，以控制COVID-19大流行。最好的策略是提供早期干预以阻止疾病的传播。通常，计算机断层扫描（CT）用于检测肺炎、肺、肺结核、肺气肿或其他胸膜（覆盖肺部的膜）疾病中的肿瘤。CT成像系统的缺点是：与MRI相比，软组织对比度较差，因为它是基于X射线的辐射暴露。肺部CT图像分割是肺部图像分析的一个必要的初始步骤。分割算法的主要挑战由于强度不均匀性、伪影的存在以及不同软组织的灰度级的接近而被夸大。本文的目标是设计和评估一种自动工具，用于使用胸部CT图像自动分割和测量COVID-19肺部感染。广泛的计算机模拟显示，与最先进的分割方法（即GraphCut，医学图像分割（MIS）和Watershed）相比，这种端到端学习方法在CT图像分割和图像增强方面具有更好的效率和灵活性。在COVID-CT数据集上进行的实验，该数据集包含（275）对COVID-19呈阳性的CT扫描以及从EL-BAYANE放射学和医学成像中心获得的新数据。用准确度、灵敏度、F-测度、精密度、MCC、Dice、Jcad和特异性得到的统计学指标的平均值分别为0.98、0.73、0.71、0.73、0.71、0.57、0.99，优于上述方法。所取得的结果证明，所提出的方法是更强大的，准确的，和直接的。
History shows that the infectious disease (COVID-19) can stun the world quickly, causing massive losses to health, resulting in a profound impact on the lives of billions of people, from both a safety and an economic perspective, for controlling the COVID-19 pandemic. The best strategy is to provide early intervention to stop the spread of the disease. In general, Computer Tomography (CT) is used to detect tumors in pneumonia, lungs, tuberculosis, emphysema, or other pleura (the membrane covering the lungs) diseases. Disadvantages of CT imaging system are: inferior soft tissue contrast compared to MRI as it is X-ray-based Radiation exposure. Lung CT image segmentation is a necessary initial step for lung image analysis. The main challenges of segmentation algorithms exaggerated due to intensity in-homogeneity, presence of artifacts, and closeness in the gray level of different soft tissue. The goal of this paper is to design and evaluate an automatic tool for automatic COVID-19 Lung Infection segmentation and measurement using chest CT images. The extensive computer simulations show better efficiency and flexibility of this end-to-end learning approach on CT image segmentation with image enhancement comparing to the state of the art segmentation approaches, namely GraphCut, Medical Image Segmentation (MIS), and Watershed. Experiments performed on COVID-CT-Dataset containing (275) CT scans that are positive for COVID-19 and new data acquired from the EL-BAYANE center for Radiology and Medical Imaging. The means of statistical measures obtained using the accuracy, sensitivity, F-measure, precision, MCC, Dice, Jacquard, and specificity are 0.98, 0.73, 0.71, 0.73, 0.71, 0.71, 0.57, 0.99 respectively; which is better than methods mentioned above. The achieved results prove that the proposed approach is more robust, accurate, and straightforward.
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DOI：
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