  Semi-supervised learning has attracted much attention in medical image
segmentation due to challenges in acquiring pixel-wise image annotations, which
is a crucial step for building high-performance deep learning methods. Most
existing semi-supervised segmentation approaches either tend to neglect
geometric constraint in object segments, leading to incomplete object coverage,
or impose strong shape prior that requires extra alignment. In this work, we
propose a novel shapeaware semi-supervised segmentation strategy to leverage
abundant unlabeled data and to enforce a geometric shape constraint on the
segmentation output. To achieve this, we develop a multi-task deep network that
jointly predicts semantic segmentation and signed distance map(SDM) of object
surfaces. During training, we introduce an adversarial loss between the
predicted SDMs of labeled and unlabeled data so that our network is able to
capture shape-aware features more effectively. Experiments on the Atrial
Segmentation Challenge dataset show that our method outperforms current
state-of-the-art approaches with improved shape estimation, which validates its
efficacy. Code is available at https://github.com/kleinzcy/SASSnet.
