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The novel coronavirus disease 2019 (COVID-19) has been spreading rapidlyaround the world and caused significant impact on the public health andeconomy. However, there is still lack of studies on effectively quantifying thelung infection caused by COVID-19. As a basic but challenging task of thediagnostic framework, segmentation plays a crucial role in accuratequantification of COVID-19 infection measured by computed tomography (CT)images. To this end, we proposed a novel deep learning algorithm for automatedsegmentation of multiple COVID-19 infection regions. Specifically, we use theAggregated Residual Transformations to learn a robust and expressive featurerepresentation and apply the soft attention mechanism to improve the capabilityof the model to distinguish a variety of symptoms of the COVID-19. With apublic CT image dataset, we validate the efficacy of the proposed algorithm incomparison with other competing methods. Experimental results demonstrate theoutstanding performance of our algorithm for automated segmentation of COVID-19Chest CT images. Our study provides a promising deep leaning-based segmentationtool to lay a foundation to quantitative diagnosis of COVID-19 lung infectionin CT images.

Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images