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.

翻译：自监督学习的最新进展表明，可以从无标签图像中学习有效的视觉表示。这引起了将自监督学习应用于医学领域的日益关注，尤其在医学图像中无标签数据丰富而标注数据难以获取的背景下。然而，大多数自监督学习方法被建模为图像级别的判别或生成代理任务，可能无法捕捉多器官分割等密集预测任务所需的细粒度表示。本文提出一种新颖的对比学习框架，集成局部区域对比（LRC）来增强现有的医学图像分割自监督预训练方法。我们的方法包括通过Felzenszwalb算法识别超像素，并使用新颖的对比采样损失执行局部对比学习。在三个多器官分割数据集上的大量实验表明，在有限标注条件下，将LRC集成到现有自监督方法中可显著提升分割性能。此外，我们证明LRC也可应用于全监督预训练方法以进一步改进性能。