改进的Mamba网络提升胎儿腹部超声图像分割精度
MS-UMamba: An Improved Vision Mamba Unet for Fetal Abdominal Medical Image Segmentation
- 融合卷积与Mamba结构，兼顾局部细节与全局上下文
- 多尺度特征融合结合空间注意力，增强关键区域表征
- 专为模糊边界和小结构设计，适合产科超声分析
近期基于Mamba的方法因其轻量设计和长程依赖建模能力，在医学图像分割中备受关注。然而，现有方法在胎儿超声图像分割中仍面临封闭解剖结构、边界模糊及微小结构等挑战。为此，我们提出MS-UMamba，一种新型混合卷积-Mamba架构用于胎儿超声图像分割。具体地，设计了集成CNN分支的视觉状态空间块（SS-MCAT-SSM），结合Mamba的全局建模优势与卷积层的局部表征能力，以提升特征学习效果。此外，提出高效的多尺度特征融合模块，融合不同层级特征并引入空间注意力机制，进一步增强模型表征能力。最后，在非公开数据集上进行大量实验，结果表明，MS-UMamba在分割性能上表现优异。
原文摘要 · Abstract (English)
Recently, Mamba-based methods have become popular in medical image segmentation due to their lightweight design and long-range dependency modeling capabilities. However, current segmentation methods frequently encounter challenges in fetal ultrasound images, such as enclosed anatomical structures, blurred boundaries, and small anatomical structures. To address the need for balancing local feature extraction and global context modeling, we propose MS-UMamba, a novel hybrid convolutional-mamba model for fetal ultrasound image segmentation. Specifically, we design a visual state space block integrated with a CNN branch (SS-MCAT-SSM), which leverages Mamba's global modeling strengths and convolutional layers' local representation advantages to enhance feature learning. In addition, we also propose an efficient multi-scale feature fusion module that integrates spatial attention mechanisms, which Integrating feature information from different layers enhances the feature representation ability of the model. Finally, we conduct extensive experiments on a non-public dataset, experimental results demonstrate that MS-UMamba model has excellent performance in segmentation performance.
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