  Due to the irregular shapes,various sizes and indistinguishable boundaries
between the normal and infected tissues, it is still a challenging task to
accurately segment the infected lesions of COVID-19 on CT images. In this
paper, a novel segmentation scheme is proposed for the infections of COVID-19
by enhancing supervised information and fusing multi-scale feature maps of
different levels based on the encoder-decoder architecture. To this end, a deep
collaborative supervision (Co-supervision) scheme is proposed to guide the
network learning the features of edges and semantics. More specifically, an
Edge Supervised Module (ESM) is firstly designed to highlight low-level
boundary features by incorporating the edge supervised information into the
initial stage of down-sampling. Meanwhile, an Auxiliary Semantic Supervised
Module (ASSM) is proposed to strengthen high-level semantic information by
integrating mask supervised information into the later stage. Then an Attention
Fusion Module (AFM) is developed to fuse multiple scale feature maps of
different levels by using an attention mechanism to reduce the semantic gaps
between high-level and low-level feature maps. Finally, the effectiveness of
the proposed scheme is demonstrated on four various COVID-19 CT datasets. The
results show that the proposed three modules are all promising. Based on the
baseline (ResUnet), using ESM, ASSM, or AFM alone can respectively increase
Dice metric by 1.12\%, 1.95\%,1.63\% in our dataset, while the integration by
incorporating three models together can rise 3.97\%. Compared with the existing
approaches in various datasets, the proposed method can obtain better
segmentation performance in some main metrics, and can achieve the best
generalization and comprehensive performance.
