Recent advancements in self-supervised learning have demonstrated that effective visual representations can be learned from unlabeled images. This has led to increased interest in applying self-supervised learning to the medical domain, where unlabeled images are abundant and labeled images are difficult to obtain. However, most self-supervised learning approaches are modeled as image level discriminative or generative proxy tasks, which may not capture the finer level representations necessary for dense prediction tasks like multi-organ segmentation. In this paper, we propose a novel contrastive learning framework that integrates Localized Region Contrast (LRC) to enhance existing self-supervised pre-training methods for medical image segmentation. Our approach involves identifying Super-pixels by Felzenszwalb's algorithm and performing local contrastive learning using a novel contrastive sampling loss. Through extensive experiments on three multi-organ segmentation datasets, we demonstrate that integrating LRC to an existing self-supervised method in a limited annotation setting significantly improves segmentation performance. Moreover, we show that LRC can also be applied to fully-supervised pre-training methods to further boost performance.

本研究提出了一种新颖的对比学习框架，它将本地化区域对比技术（LRC）融入到现有的半监督自监督预训练方法中，以增强医学图像分割的性能。通过在三个多器官分割数据集上的大量实验，我们证明在受限注释的情况下将LRC集成到自监督方法中可以显著提高分割性能。此外，我们还表明LRC也可以应用于完全监督的预训练方法以进一步提高性能。

增强自监督学习的医学图像分割本地化区域对比