  Coronavirus Disease 2019 (COVID-19) has rapidly spread in 2020, emerging a
mass of studies for lung infection segmentation from CT images. Though many
methods have been proposed for this issue, it is a challenging task because of
infections of various size appearing in different lobe zones. To tackle these
issues, we propose a Graph-based Pyramid Global Context Reasoning (Graph-PGCR)
module, which is capable of modeling long-range dependencies among disjoint
infections as well as adapt size variation. We first incorporate graph
convolution to exploit long-term contextual information from multiple lobe
zones. Different from previous average pooling or maximum object probability,
we propose a saliency-aware projection mechanism to pick up infection-related
pixels as a set of graph nodes. After graph reasoning, the relation-aware
features are reversed back to the original coordinate space for the down-stream
tasks. We further construct multiple graphs with different sampling rates to
handle the size variation problem. To this end, distinct multi-scale long-range
contextual patterns can be captured. Our Graph-PGCR module is plug-and-play,
which can be integrated into any architecture to improve its performance.
Experiments demonstrated that the proposed method consistently boost the
performance of state-of-the-art backbone architectures on both of public and
our private COVID-19 datasets.
