  In medical image segmentation, supervised deep networks' success comes at the
cost of requiring abundant labeled data. While asking domain experts to
annotate only one or a few of the cohort's images is feasible, annotating all
available images is impractical. This issue is further exacerbated when
pre-trained deep networks are exposed to a new image dataset from an unfamiliar
distribution. Using available open-source data for ad-hoc transfer learning or
hand-tuned techniques for data augmentation only provides suboptimal solutions.
Motivated by atlas-based segmentation, we propose a novel volumetric
self-supervised learning for data augmentation capable of synthesizing
volumetric image-segmentation pairs via learning transformations from a single
labeled atlas to the unlabeled data. Our work's central tenet benefits from a
combined view of one-shot generative learning and the proposed self-supervised
training strategy that cluster unlabeled volumetric images with similar styles
together. Unlike previous methods, our method does not require input volumes at
inference time to synthesize new images. Instead, it can generate diversified
volumetric image-segmentation pairs from a prior distribution given a single or
multi-site dataset. Augmented data generated by our method used to train the
segmentation network provide significant improvements over state-of-the-art
deep one-shot learning methods on the task of brain MRI segmentation. Ablation
studies further exemplified that the proposed appearance model and joint
training are crucial to synthesize realistic examples compared to existing
medical registration methods. The code, data, and models are available at
https://github.com/devavratTomar/SST.
