  A novel coronavirus disease 2019 (COVID-19) was detected and has spread
rapidly across various countries around the world since the end of the year
2019, Computed Tomography (CT) images have been used as a crucial alternative
to the time-consuming RT-PCR test. However, pure manual segmentation of CT
images faces a serious challenge with the increase of suspected cases,
resulting in urgent requirements for accurate and automatic segmentation of
COVID-19 infections. Unfortunately, since the imaging characteristics of the
COVID-19 infection are diverse and similar to the backgrounds, existing medical
image segmentation methods cannot achieve satisfactory performance. In this
work, we try to establish a new deep convolutional neural network tailored for
segmenting the chest CT images with COVID-19 infections. We firstly maintain a
large and new chest CT image dataset consisting of 165,667 annotated chest CT
images from 861 patients with confirmed COVID-19. Inspired by the observation
that the boundary of the infected lung can be enhanced by adjusting the global
intensity, in the proposed deep CNN, we introduce a feature variation block
which adaptively adjusts the global properties of the features for segmenting
COVID-19 infection. The proposed FV block can enhance the capability of feature
representation effectively and adaptively for diverse cases. We fuse features
at different scales by proposing Progressive Atrous Spatial Pyramid Pooling to
handle the sophisticated infection areas with diverse appearance and shapes. We
conducted experiments on the data collected in China and Germany and show that
the proposed deep CNN can produce impressive performance effectively.
