  The novel coronavirus disease 2019 (COVID-19) characterized by atypical
pneumonia has caused millions of deaths worldwide. Automatically segmenting
lesions from chest Computed Tomography (CT) is a promising way to assist
doctors in COVID-19 screening, treatment planning, and follow-up monitoring.
However, voxel-wise annotations are extremely expert-demanding and scarce,
especially when it comes to novel diseases, while an abundance of unlabeled
data could be available. To tackle the challenge of limited annotations, in
this paper, we propose an uncertainty-guided dual-consistency learning network
(UDC-Net) for semi-supervised COVID-19 lesion segmentation from CT images.
Specifically, we present a dual-consistency learning scheme that simultaneously
imposes image transformation equivalence and feature perturbation invariance to
effectively harness the knowledge from unlabeled data. We then quantify both
the epistemic uncertainty and the aleatoric uncertainty and employ them
together to guide the consistency regularization for more reliable unsupervised
learning. Extensive experiments showed that our proposed UDC-Net improves the
fully supervised method by 6.3% in Dice and outperforms other competitive
semi-supervised approaches by significant margins, demonstrating high potential
in real-world clinical practice.
