  Deep learning has demonstrated significant improvements in medical image
segmentation using a sufficiently large amount of training data with manual
labels. Acquiring well-representative labels requires expert knowledge and
exhaustive labors. In this paper, we aim to boost the performance of
semi-supervised learning for medical image segmentation with limited labels
using a self-ensembling contrastive learning technique. To this end, we propose
to train an encoder-decoder network at image-level with small amounts of
labeled images, and more importantly, we learn latent representations directly
at feature-level by imposing contrastive loss on unlabeled images. This method
strengthens intra-class compactness and inter-class separability, so as to get
a better pixel classifier. Moreover, we devise a student encoder for online
learning and an exponential moving average version of it, called teacher
encoder, to improve the performance iteratively in a self-ensembling manner. To
construct contrastive samples with unlabeled images, two sampling strategies
that exploit structure similarity across medical images and utilize
pseudo-labels for construction, termed region-aware and anatomical-aware
contrastive sampling, are investigated. We conduct extensive experiments on an
MRI and a CT segmentation dataset and demonstrate that in a limited label
setting, the proposed method achieves state-of-the-art performance. Moreover,
the anatomical-aware strategy that prepares contrastive samples on-the-fly
using pseudo-labels realizes better contrastive regularization on feature
representations.
