Hemodynamic-Driven Multi-prototypes Learning for One-Shot Segmentation in Breast Cancer DCE-MRI

In dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast, tumor segmentation is pivotal in screening and prognostic evaluation. However, automated segmentation is typically limited by a large amount of fully annotated data, and the multi-connected regions and complicated contours of tumors also pose a significant challenge. Existing few-shot segmentation methods tend to overfit the targets of base categories, resulting in inaccurate segmentation boundaries. In this work, we propose a hemodynamic-driven multi-prototypes network (HDMPNet) for one-shot segmentation that generates high-quality segmentation maps even for tumors of variable size, appearance, and shape. Specifically, a parameter-free module, called adaptive superpixel clustering (ASC), is designed to extract multi-prototypes by aggregating similar feature vectors for the multi-connected regions. Moreover, we develop a cross-fusion decoder (CFD) for optimizing boundary segmentation, which involves reweighting and aggregating support and query features. Besides, a bidirectional Gate Recurrent Unit is employed to acquire pharmacokinetic knowledge, subsequently driving the ASC and CFD modules. Experiments on two public breast cancer datasets show that our method yields higher segmentation performance than the existing stateof-the-art methods. The source code will be available on https://github.com/Medical-AI-Lab-of-JNU/HDMP.