  The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly
around the world and caused significant impact on the public health and
economy. However, there is still lack of studies on effectively quantifying the
lung infection caused by COVID-19. As a basic but challenging task of the
diagnostic framework, segmentation plays a crucial role in accurate
quantification of COVID-19 infection measured by computed tomography (CT)
images. To this end, we proposed a novel deep learning algorithm for automated
segmentation of multiple COVID-19 infection regions. Specifically, we use the
Aggregated Residual Transformations to learn a robust and expressive feature
representation and apply the soft attention mechanism to improve the capability
of the model to distinguish a variety of symptoms of the COVID-19. With a
public CT image dataset, we validate the efficacy of the proposed algorithm in
comparison with other competing methods. Experimental results demonstrate the
outstanding performance of our algorithm for automated segmentation of COVID-19
Chest CT images. Our study provides a promising deep leaning-based segmentation
tool to lay a foundation to quantitative diagnosis of COVID-19 lung infection
in CT images.
