MS-UMamba: An Improved Vision Mamba Unet for Fetal Abdominal Medical Image Segmentation
Journal:
arXiv
Published Date:
Jun 14, 2025
Abstract
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.
