Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning

TL;DR

This paper evaluates inpainting-based self-supervised learning methods for tissue segmentation in CT and MRI images, showing they outperform traditional supervised methods in label-limited scenarios.

Contribution

It demonstrates that inpainting-based SSL pretext tasks can effectively improve tissue segmentation performance with limited labeled data in medical imaging.

Findings

SSL models outperform supervised methods in low-label scenarios

Inpainting tasks improve segmentation accuracy on MRI and CT images

Optimal SSL implementation enhances clinical segmentation metrics

Abstract

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.