  Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing
the world to face an existential health crisis. Automated detection of lung
infections from computed tomography (CT) images offers a great potential to
augment the traditional healthcare strategy for tackling COVID-19. However,
segmenting infected regions from CT slices faces several challenges, including
high variation in infection characteristics, and low intensity contrast between
infections and normal tissues. Further, collecting a large amount of data is
impractical within a short time period, inhibiting the training of a deep
model. To address these challenges, a novel COVID-19 Lung Infection
Segmentation Deep Network (Inf-Net) is proposed to automatically identify
infected regions from chest CT slices. In our Inf-Net, a parallel partial
decoder is used to aggregate the high-level features and generate a global map.
Then, the implicit reverse attention and explicit edge-attention are utilized
to model the boundaries and enhance the representations. Moreover, to alleviate
the shortage of labeled data, we present a semi-supervised segmentation
framework based on a randomly selected propagation strategy, which only
requires a few labeled images and leverages primarily unlabeled data. Our
semi-supervised framework can improve the learning ability and achieve a higher
performance. Extensive experiments on our COVID-SemiSeg and real CT volumes
demonstrate that the proposed Inf-Net outperforms most cutting-edge
segmentation models and advances the state-of-the-art performance.
