Enhancing Long-range Dependency for Biomedical Image Segmentation
1Peter Munk Cardiac Centre, University Health Network, Toronto,
Canada
2Department of Laboratory Medicine and Pathobiology, University of
Toronto,
Toronto, Canada
3Vector Institute for Artificial Intelligence, Toronto,
Canada
4Department of Computer Science, University of Toronto, Toronto,
Canada
5AI Hub, University Health Network, Toronto, Canada
bowang@vectorinstitute.ai
Overview of the U-Mamba (Enc) architecture. a. U-Mamba building block contains two successive Residual blocks followed by a Mamba block to enhance long-range dependencies. b. The whole architecture of U-Mamba, which is a typical encoder-decoder framework with U-Mamba blocks in the encoder, Residual blocks in the decoder, together with skip connections.
We evaluate U-Mamba on four publicly available datasets, covering four modalities and various targets. U-Mamba inherits the self-configuring feature from nnU-Net, which can automatically generate networks for different datasets.
Dataset | Dimension | #Training Image | #Testing Image | #Targets |
---|---|---|---|---|
Abdomen CT | 3D | 50 (4794 slices) | 50 (10894 slices) | 13 |
Abdomen MRI | 3D | 60 (5615 slices) | 50 (3357 slices) | 13 |
Endoscopy Images | 2D | 1800 | 1200 | 7 |
Microscopy Images | 2D | 1000 | 101 | 2 |
Configurations | Patch size | Batch size | # Stages | # Pooling per axis |
---|---|---|---|---|
Abdomen CT | (40, 224, 192) | 2 | 6 | (3, 3, 5) |
3D Abdomen MR | (48, 160, 224) | 2 | 6 | (3, 5, 5) |
2D Abdomen MR | (320, 320) | 30 | 7 | (6, 6) |
Endoscopy | (384, 640) | 13 | 7 | (6, 6) |
Microscopy | (512, 512) | 12 | 8 | (7, 7) |
Visualized results demonstrate that U-Mamba can handle complex targets with fewer segmentation outliers.
@article{U-Mamba,
title={U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation},
author={Ma, Jun and Li, Feifei and Wang, Bo},
journal={arXiv preprint arXiv:2401.04722},
year={2024}
}
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