  Automated segmentation in medical image analysis is a challenging task that
requires a large amount of manually labeled data. However, most existing
learning-based approaches usually suffer from limited manually annotated
medical data, which poses a major practical problem for accurate and robust
medical image segmentation. In addition, most existing semi-supervised
approaches are usually not robust compared with the supervised counterparts,
and also lack explicit modeling of geometric structure and semantic
information, both of which limit the segmentation accuracy. In this work, we
present SimCVD, a simple contrastive distillation framework that significantly
advances state-of-the-art voxel-wise representation learning. We first describe
an unsupervised training strategy, which takes two views of an input volume and
predicts their signed distance maps of object boundaries in a contrastive
objective, with only two independent dropout as mask. This simple approach
works surprisingly well, performing on the same level as previous fully
supervised methods with much less labeled data. We hypothesize that dropout can
be viewed as a minimal form of data augmentation and makes the network robust
to representation collapse. Then, we propose to perform structural distillation
by distilling pair-wise similarities. We evaluate SimCVD on two popular
datasets: the Left Atrial Segmentation Challenge (LA) and the NIH pancreas CT
dataset. The results on the LA dataset demonstrate that, in two types of
labeled ratios (i.e., 20% and 10%), SimCVD achieves an average Dice score of
90.85% and 89.03% respectively, a 0.91% and 2.22% improvement compared to
previous best results. Our method can be trained in an end-to-end fashion,
showing the promise of utilizing SimCVD as a general framework for downstream
tasks, such as medical image synthesis, enhancement, and registration.
