UltraMamba: Mamba-Based Multimodal Ultrasound Image Adaptive Fusion for Breast Lesion Segmentation

Multimodal ultrasound imaging, combining B-mode ultrasound, shear wave velocity, and shear wave time, is crucial for diagnosing and treating breast lesions, providing insights into lesion characteristics and tissue properties. However, challenges arise from inter-modal feature misalignment and attention shifts due to varied capture methods and an overemphasis on vibrant color data. To tackle these issues, we introduce two innovations: a novel segmentation framework and a comprehensive dataset. The UltraMamba framework utilizes bidirectional alignment between modalities and enhances region-specific information to improve breast lesion segmentation accuracy. Key components include the Cross-Modal Knowledge Interaction module for robust information exchange and the Region-Aware Feature Excitation module to focus on relevant features. We also present the BreLS dataset, the first two-dimensional multimodal ultrasound breast lesion dataset, with paired images from 506 cases, serving as a valuable resource for analysis. UltraMamba shows strong performance on the BreLS dataset, achieving a Dice Similarity Coefficient of 72.16% and an HD95 of 42.02 mm, reflecting improvements of 2.59% in DSC and a 6.78 mm reduction in HD95 compared to the second-best framework, MMCA-NET. These results highlight UltraMamba's potential to enhance segmentation accuracy in clinical settings, facilitating precise treatment planning and, ultimately, leading to improved outcomes. Code: https://github.com/deepang-ai/UltraMamba.