Contrastive Learning-Based Breast Tumor Segmentation in DCE-MRI

Precise and automated segmentation of tumors from breast dynamic contrast-enhanced magnetic resonance images (DCE-MRI) is crucial for obtaining quantitative morphological and functional information, thereby assisting subsequent diagnosis and treatment. However, many existing methods mainly focus on features within tumor regions and neglect enhanced background tissues, leading to the potential over-segmentation problem. To better distinguish tumor tissues from complex background structures (e.g., enhanced vessels), we propose a novel approach based on contrastive feature learning. Our method involves pre-training a highly sensitive encoder using contrastive learning, where tumor and background patches are utilized as paired positive-negative samples, to emphasize tumor tissues and to enhance their discriminative features. Furthermore, the well-trained encoder is employed for accurate tumor segmentation by using a feature fusion module in a global-to-local manner. Through extensive validations using a large dataset of breast DCE-MRI scans, our proposed model demonstrates superior segmentation performance, effectively reducing over-segmentation on enhanced tissue regions as expected.