  Although supervised learning has enabled high performance for image
segmentation, it requires a large amount of labeled training data, which can be
difficult to obtain in the medical imaging field. Self-supervised learning
(SSL) methods involving pretext tasks have shown promise in overcoming this
requirement by first pretraining models using unlabeled data. In this work, we
evaluate the efficacy of two SSL methods (inpainting-based pretext tasks of
context prediction and context restoration) for CT and MRI image segmentation
in label-limited scenarios, and investigate the effect of implementation design
choices for SSL on downstream segmentation performance. We demonstrate that
optimally trained and easy-to-implement inpainting-based SSL segmentation
models can outperform classically supervised methods for MRI and CT tissue
segmentation in label-limited scenarios, for both clinically-relevant metrics
and the traditional Dice score.
