Feature-enhanced multi-sequence MRI-based fusion mechanism for breast tumor segmentation

Multi-sequence MRI plays a crucial role in the effective segmentation of breast tumors, contributing to accurate clinical diagnosis and treatment. However, the problem of missing certain sequence images may occur in clinical practice, leading to a potential impact on network performance. In medical imaging tasks, researchers are prone to ignore the correlation between different sequences, resulting in lack of expression of the extracted features. To address these problems, we propose the Feature-enhanced Multi-sequence Feature Fusion Network (FMFF-Net), which fuses information from dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) sequences to improve the accuracy of breast tumor segmentation. Utilizing an attention mechanism and adversarial learning, FMFF-Net identifies potential mappings and relationships between sequences, generating features for missing sequences and enhancing feature representation. The network incorporates a High-Frequency Edge Attention Block (HFAB) to accentuate tumor edge details, leading to more precise segmentation. Tested on a dataset of 98 high-risk breast cancer MRI images, FMFF-Net demonstrated superior performance, with a Dice Similarity Coefficient (DSC) of 83.6%, Intersection over Union (IoU) of 73.4%, F1 score of 88.1%, and Sensitivity of 74.1%. Comparative analysis against mainstream segmentation methods revealed that our proposed FMFF-Net exhibits a competitive edge in the field of breast tumor segmentation.