HRCUNet: Hierarchical Region Contrastive Learning for Segmentation of Breast Tumors in DCE-MRI

Abstract

Segmenting breast tumors from dynamic contrast-enhanced magnetic resonance images is a critical step in the early detection and diagnosis of breast cancer. However, this task becomes significantly more challenging due to the diverse shapes and sizes of tumors, which make it difficult to establish a unified perception field for modeling them. Moreover, tumor regions are often subtle or imperceptible during early detection, exacerbating the issue of extreme class imbalance. This imbalance can lead to biased training and challenge accurately segmenting tumor regions from the predominant normal tissues. To address these issues, we propose a hierarchical region contrastive learning approach for breast tumor segmentation. Our approach introduces a novel hierarchical region contrastive learning loss function that addresses the class imbalance problem. This loss function encourages the model to create a clear separation between feature embeddings by maximizing the inter-class margin and minimizing the intra-class distance across different levels of the feature space. In addition, we design a novel Attention-based 3D Multi-scale Feature Fusion Residual Module to explore more granular multi-scale representations to improve the feature learning ability of tumors. Extensive experiments on two breast DCE-MRI datasets demonstrate that the proposed algorithm is more competitive against several state-of-the-art approaches under different segmentation metrics.