  Deep learning has revolutionized medical image segmentation, but it relies
heavily on high-quality annotations. The time, cost and expertise required to
label images at the pixel-level for each new task has slowed down widespread
adoption of the paradigm. We propose Pix2Rep, a self-supervised learning (SSL)
approach for few-shot segmentation, that reduces the manual annotation burden
by learning powerful pixel-level representations directly from unlabeled
images. Pix2Rep is a novel pixel-level loss and pre-training paradigm for
contrastive SSL on whole images. It is applied to generic encoder-decoder deep
learning backbones (e.g., U-Net). Whereas most SSL methods enforce invariance
of the learned image-level representations under intensity and spatial image
augmentations, Pix2Rep enforces equivariance of the pixel-level
representations. We demonstrate the framework on a task of cardiac MRI
segmentation. Results show improved performance compared to existing semi- and
self-supervised approaches; and a 5-fold reduction in the annotation burden for
equivalent performance versus a fully supervised U-Net baseline. This includes
a 30% (resp. 31%) DICE improvement for one-shot segmentation under
linear-probing (resp. fine-tuning). Finally, we also integrate the novel
Pix2Rep concept with the Barlow Twins non-contrastive SSL, which leads to even
better segmentation performance.
