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DyMamba: dynamic Mamba for microscopy image semantic segmentation
Buqing Cai1,2, Xingsheng Wang1,2, Zhuo Jia1,2
1Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Ministry of Education, Beijing Institute of Technology, Beijing, 100081, China.
Bioinformatics (Oxford, England)
|June 17, 2026
Summary
DyMamba introduces a dynamic scanning strategy for Mamba-based microscopy image segmentation. This novel approach improves pixel-level segmentation accuracy for cells, organelles, and tissues, outperforming existing methods.
Area of Science:
- Computational Biology
- Image Analysis
- Deep Learning
Background:
- Accurate segmentation of cellular structures in microscopy is vital for biological research.
- Mamba architecture, based on State Space Models (SSMs), excels at modeling long-range dependencies but faces limitations in vision tasks due to fixed scanning strategies.
- Current Mamba scanning methods (raster, local) cause spatial discontinuities, hindering pixel-level segmentation effectiveness, especially for dense structures.
Purpose of the Study:
- To develop an advanced Mamba-based model for improved microscopy image segmentation.
- To address the limitations of static scanning strategies in Mamba architectures for vision tasks.
- To enhance the segmentation of fine details and small objects in biological images.
Main Methods:
- Propose DyMamba, a novel Mamba-based model incorporating a dynamic scanning strategy that adapts paths based on local image features and complexity.
- Introduce a local-aware module for pixel-level regional processing to improve detail and small object segmentation.
- Validate DyMamba on diverse microscopy image datasets at cell, organelle, and tissue scales.
Main Results:
- DyMamba demonstrates robust segmentation performance across various microscopy image types.
- Experiments on six datasets show DyMamba significantly outperforms state-of-the-art methods.
- Achieved an average improvement of 6.9% in mDice and 4.3% in mIoU compared to existing approaches.
Conclusions:
- DyMamba's dynamic scanning strategy effectively overcomes spatial discontinuities in Mamba-based segmentation.
- The model shows superior performance in segmenting complex microscopy images, including fine details and small objects.
- DyMamba offers a promising advancement for automated analysis in biological imaging research.