  The spread of COVID-19 has brought a huge disaster to the world, and the
automatic segmentation of infection regions can help doctors to make diagnosis
quickly and reduce workload. However, there are several challenges for the
accurate and complete segmentation, such as the scattered infection area
distribution, complex background noises, and blurred segmentation boundaries.
To this end, in this paper, we propose a novel network for automatic COVID-19
lung infection segmentation from CT images, named BCS-Net, which considers the
boundary, context, and semantic attributes. The BCS-Net follows an
encoder-decoder architecture, and more designs focus on the decoder stage that
includes three progressively Boundary-Context-Semantic Reconstruction (BCSR)
blocks. In each BCSR block, the attention-guided global context (AGGC) module
is designed to learn the most valuable encoder features for decoder by
highlighting the important spatial and boundary locations and modeling the
global context dependence. Besides, a semantic guidance (SG) unit generates the
semantic guidance map to refine the decoder features by aggregating multi-scale
high-level features at the intermediate resolution. Extensive experiments
demonstrate that our proposed framework outperforms the existing competitors
both qualitatively and quantitatively.
