Distribution-Aware Contrastive Learning for Robust Medical Image Segmentation

Medical image segmentation is pivotal in quantifying tissue volumes, facilitating diagnoses, and enabling other critical medical applications. However, accurately segmenting medical images can be challenging because the complex intensity distribution inherent in the data arises from the highly complex interaction of many latent factors (data heterogeneity). In this context, we propose a novel method called Distribution-aware Contrastive Learning for Robust Segmentation (DCL-Seg) to address the inconsistency in medical image segmentation. Based on the assumption of content separability, we use learnable parameters to construct positive samples with a potential structure invariance via contrastive learning. In this way, our method can mitigate the negative effects of data heterogeneity to separate overlapped class distribution and structural solid boundary. We are in one public dataset and two clinical datasets for Breast tumor and Retinal vessel segmentation, which have achieved excellent results and widely proved the superiority of our method.