  The success of deep learning methods in medical image segmentation tasks
usually requires a large amount of labeled data. However, obtaining reliable
annotations is expensive and time-consuming. Semi-supervised learning has
attracted much attention in medical image segmentation by taking the advantage
of unlabeled data which is much easier to acquire. In this paper, we propose a
novel dual-task mutual learning framework for semi-supervised medical image
segmentation. Our framework can be formulated as an integration of two
individual segmentation networks based on two tasks: learning region-based
shape constraint and learning boundary-based surface mismatch. Different from
the one-way transfer between teacher and student networks, an ensemble of
dual-task students can learn collaboratively and implicitly explore useful
knowledge from each other during the training process. By jointly learning the
segmentation probability maps and signed distance maps of targets, our
framework can enforce the geometric shape constraint and learn more reliable
information. Experimental results demonstrate that our method achieves
performance gains by leveraging unlabeled data and outperforms the
state-of-the-art semi-supervised segmentation methods.
