Inf-Seg: Automatic segmentation and quantification method for CT-based COVID-19 diagnosis

The global spread of the COVID-19 has increased the need for physicians and accurate and efficient diagnostic tools. The best way to control the spread of COVID-19 is through public vaccination as well as early intervention to prevent the spread of the disease. According to the World Health Organization, chest CT scans in the early stages of COVID-19 disease have good accuracy, which leads to the widespread use of these images in the diagnostics and evaluation of COVID-19 disease. Lung CT scan segmentation is an essential first step for lung image analysis. The purpose of this article is to evaluate the existing computer systems and to present a more efficient computer system for CT scan image segmentation. For this propose, a novel artificial intelligence (AI)-based COVID-19 Lung Infection Segmentation (Inf-Seg) method is proposed to automatically identify infected regions from chest CT scan. In Inf-Seg, after pre-processing of medical image and improving the image quality, texture feature extraction methods are used to collect high-level features and generate a global map. In the next step, we used YOLACT, which consists of a backbone part of a network of feature pyramids for creating multi-scale feature maps and efficient classification and localization of objects of various sizes (with better information than a regular feature pyramid for object detection), a Protonet part and prediction.