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MLAgg-UNet: Advancing Medical Image Segmentation With Efficient Transformer and Mamba-Inspired Multi-Scale Sequence
IEEE Journal of Biomedical and Health Informatics
|August 7, 2025
Summary
The Mamba-Like Aggregated UNet (MLAgg-UNet) enhances medical image segmentation by integrating Mamba mechanisms into a U-shaped architecture, improving accuracy and efficiency over existing methods.
Area of Science:
- Medical image analysis
- Deep learning for computer vision
- Biomedical engineering
Background:
- Transformers and state space sequence models (SSMs) show promise in biomedical image segmentation for long-range dependency capture.
- Traditional visual state space (VSS) models face challenges with image token compatibility and autoregressive assumptions.
- Transformer models, while not requiring autoregressive assumptions, incur high computational costs limiting channel-wise information use.
Purpose of the Study:
- To introduce a novel deep learning architecture, MLAgg-UNet, for improved biomedical image segmentation.
- To address limitations of existing methods, including computational cost and feature representation.
- To enhance the utilization of channel-wise information and multi-scale feature integration.
Main Methods:
- Proposed the Mamba-Like Aggregated UNet (MLAgg-UNet) incorporating Mamba-inspired mechanisms.
- Developed the Mamba-Like Aggregated Attention (MLAgg) block for efficient token dependency modeling.
- Introduced the Multi-Scale Mamba Module with Implicit Causality (MSMM) within skip connections for cross-scale feature optimization.
Main Results:
- MLAgg-UNet demonstrated superior performance across four benchmark datasets (AbdomenMRI, ACDC, BTCV, EndoVis17) covering MRI, CT, and endoscopy.
- Achieved significant Dice Similarity Coefficient (DSC) score improvements: 1.24% (AbdomenMRI), 0.20% (ACDC), 0.33% (BTCV), and 0.39% (EndoVis17).
- Outperformed state-of-the-art CNN, Transformer, and Mamba-based segmentation methods.
Conclusions:
- MLAgg-UNet effectively captures feature correlations and integrates complementary multi-scale information for robust medical image segmentation.
- The proposed architecture offers a balance between representational ability and computational efficiency.
- The study provides a promising solution for various medical imaging modalities, with publicly available implementation.
