What is it about?

In order toreduce the labor intensity of radiologists, improve the accuracy of COVID-19 diagnosis,we take the Res2Net as the backbone and elaborate a network Ref-Net to implement automatic COVID-19 CT image segmentation.The network we designed using edge attention, location attention and context exploration technology has obtained more accurate segmentation results on the dataset.

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Why is it important?

COVID-19 is threatening the health of the global people, and bringing great losses to our economy and society. However, computed tomography (CT) image segmentation can make clinicians quickly identify the COVID-19 infected regions. Accurate segmentation infection area of COVID-19 can contribute screen confirmed cases.In addition, automatic segmentation technology can reduce the labor intensity of radiologists, improve the accuracy of COVID-19 diagnosis, and save precious time for the patients. And, it is necessary to assist radiologists in labeling lung infections by using automatic segmentation techniques.


This article is aimed at COVID-19, an issue of worldwide concerned. Therefore, the research of this article is very meaningful.We propose a segmentation network for segmenting COVID-19 infected areas in CT images. We hoped that our method can help clinicians screen infected patients and reduce their burden.

shangwang liu

Read the Original

This page is a summary of: COVID‐19 CT image segmentation based on improved Res2Net, Medical Physics, August 2022, Wiley,
DOI: 10.1002/mp.15882.
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