UU-Mamba: Uncertainty-aware U-Mamba for Cardiac Image Segmentation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912003393585152 |
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| author | Tsai, Ting Yu Lin, Li Hu, Shu Chang, Ming-Ching Zhu, Hongtu Wang, Xin |
| author_facet | Tsai, Ting Yu Lin, Li Hu, Shu Chang, Ming-Ching Zhu, Hongtu Wang, Xin |
| contents | Biomedical image segmentation is critical for accurate identification and analysis of anatomical structures in medical imaging, particularly in cardiac MRI. Manual segmentation is labor-intensive, time-consuming, and prone to errors, highlighting the need for automated methods. However, current machine learning approaches face challenges like overfitting and data demands. To tackle these issues, we propose a new UU-Mamba model, integrating the U-Mamba model with the Sharpness-Aware Minimization (SAM) optimizer and an uncertainty-aware loss function. SAM enhances generalization by locating flat minima in the loss landscape, thus reducing overfitting. The uncertainty-aware loss combines region-based, distribution-based, and pixel-based loss designs to improve segmentation accuracy and robustness. Evaluation of our method is performed on the ACDC cardiac dataset, outperforming state-of-the-art models including TransUNet, Swin-Unet, nnUNet, and nnFormer. Our approach achieves Dice Similarity Coefficient (DSC) and Mean Squared Error (MSE) scores, demonstrating its effectiveness in cardiac MRI segmentation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17496 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | UU-Mamba: Uncertainty-aware U-Mamba for Cardiac Image Segmentation Tsai, Ting Yu Lin, Li Hu, Shu Chang, Ming-Ching Zhu, Hongtu Wang, Xin Image and Video Processing Biomedical image segmentation is critical for accurate identification and analysis of anatomical structures in medical imaging, particularly in cardiac MRI. Manual segmentation is labor-intensive, time-consuming, and prone to errors, highlighting the need for automated methods. However, current machine learning approaches face challenges like overfitting and data demands. To tackle these issues, we propose a new UU-Mamba model, integrating the U-Mamba model with the Sharpness-Aware Minimization (SAM) optimizer and an uncertainty-aware loss function. SAM enhances generalization by locating flat minima in the loss landscape, thus reducing overfitting. The uncertainty-aware loss combines region-based, distribution-based, and pixel-based loss designs to improve segmentation accuracy and robustness. Evaluation of our method is performed on the ACDC cardiac dataset, outperforming state-of-the-art models including TransUNet, Swin-Unet, nnUNet, and nnFormer. Our approach achieves Dice Similarity Coefficient (DSC) and Mean Squared Error (MSE) scores, demonstrating its effectiveness in cardiac MRI segmentation. |
| title | UU-Mamba: Uncertainty-aware U-Mamba for Cardiac Image Segmentation |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2405.17496 |