Saood Adnan, Hatem Iyad
Mechatronics Program for the Distinguished, Tishreen University, Distinction and Creativity Agency, Latakia, Syria.
BMC Med Imaging. 2021 Feb 9;21(1):19. doi: 10.1186/s12880-020-00529-5.
Currently, there is an urgent need for efficient tools to assess the diagnosis of COVID-19 patients. In this paper, we present feasible solutions for detecting and labeling infected tissues on CT lung images of such patients. Two structurally-different deep learning techniques, SegNet and U-NET, are investigated for semantically segmenting infected tissue regions in CT lung images.
We propose to use two known deep learning networks, SegNet and U-NET, for image tissue classification. SegNet is characterized as a scene segmentation network and U-NET as a medical segmentation tool. Both networks were exploited as binary segmentors to discriminate between infected and healthy lung tissue, also as multi-class segmentors to learn the infection type on the lung. Each network is trained using seventy-two data images, validated on ten images, and tested against the left eighteen images. Several statistical scores are calculated for the results and tabulated accordingly.
The results show the superior ability of SegNet in classifying infected/non-infected tissues compared to the other methods (with 0.95 mean accuracy), while the U-NET shows better results as a multi-class segmentor (with 0.91 mean accuracy).
Semantically segmenting CT scan images of COVID-19 patients is a crucial goal because it would not only assist in disease diagnosis, also help in quantifying the severity of the illness, and hence, prioritize the population treatment accordingly. We propose computer-based techniques that prove to be reliable as detectors for infected tissue in lung CT scans. The availability of such a method in today's pandemic would help automate, prioritize, fasten, and broaden the treatment of COVID-19 patients globally.
目前，迫切需要高效工具来评估新冠病毒肺炎患者的诊断情况。在本文中，我们提出了在这类患者的肺部CT图像上检测和标记感染组织的可行解决方案。我们研究了两种结构不同的深度学习技术，即SegNet和U-NET，用于对肺部CT图像中的感染组织区域进行语义分割。
我们提议使用两种已知的深度学习网络，SegNet和U-NET，进行图像组织分类。SegNet是一个场景分割网络，U-NET是一种医学分割工具。这两种网络都被用作二分类分割器来区分感染的和健康的肺组织，也被用作多分类分割器来识别肺部的感染类型。每个网络使用72幅数据图像进行训练，在10幅图像上进行验证，并针对其余18幅图像进行测试。针对结果计算了几个统计分数并相应列表。
结果表明，与其他方法相比，SegNet在对感染/未感染组织进行分类方面具有更强的能力（平均准确率为0.95），而U-NET作为多分类分割器表现出更好的结果（平均准确率为0.91）。
对新冠病毒肺炎患者的CT扫描图像进行语义分割是一个关键目标，因为这不仅有助于疾病诊断，还能帮助量化疾病的严重程度，从而相应地对患者治疗进行优先级排序。我们提出的基于计算机的技术被证明是可靠的肺部CT扫描感染组织检测器。在当今的疫情大流行中，这种方法的可用性将有助于在全球范围内实现新冠病毒肺炎患者治疗的自动化、优先级排序、加速和扩大。
