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Multimodal ultrasound imaging, combining B-mode ultrasound, shear wave velocity, and shear wave time, plays a crucial role in breast tumor diagnosis by providing detailed insights into tumor characteristics. However, challenges such as inter-modal feature misalignment and attention drift complicate accurate segmentation due to variations in how each modality captures tissue properties. To address issues, we propose the MusoMamba framework and the MUB2025 dataset. MusoMamba enhances segmentation accuracy through bidirectional alignment and region-specific feature enhancement, utilizing the Modality-Collaborative Learning and Modality-Guided Enhancement modules. The MUB2025 dataset, comprising paired images across the three modalities from 506 cases, supports detailed analysis and model training. MusoMamba achieves strong results on MUB2025, with a 72.67% Dice Similarity Coefficient and a 39.42 mm HD95, improving DSC by 3.24% and reducing HD95 by 5.37 mm over the second-best framework. These advancements demonstrate MusoMamba’s potential to enhance segmentation accuracy in clinical settings.

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MusoMamba: Mamba-Based Multimodal Ultrasound Image Mutual Learning for Breast Tumor Segmentation

  • 会议论文

  • 发表日期：

    2026年

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  • Jiahui Huang,
  • Yuchen Jiang,
  • Jiaxin Huang,
  • Mingdu Zhang,
  • Jufeng Yang,
  • Qiong Wang,
  • Xiaoqing Pei,
  • Yan Pang

摘要

Multimodal ultrasound imaging, combining B-mode ultrasound, shear wave velocity, and shear wave time, plays a crucial role in breast tumor diagnosis by providing detailed insights into tumor characteristics. However, challenges such as inter-modal feature misalignment and attention drift complicate accurate segmentation due to variations in how each modality captures tissue properties. To address issues, we propose the MusoMamba framework and the MUB2025 dataset. MusoMamba enhances segmentation accuracy through bidirectional alignment and region-specific feature enhancement, utilizing the Modality-Collaborative Learning and Modality-Guided Enhancement modules. The MUB2025 dataset, comprising paired images across the three modalities from 506 cases, supports detailed analysis and model training. MusoMamba achieves strong results on MUB2025, with a 72.67% Dice Similarity Coefficient and a 39.42 mm HD95, improving DSC by 3.24% and reducing HD95 by 5.37 mm over the second-best framework. These advancements demonstrate MusoMamba’s potential to enhance segmentation accuracy in clinical settings.