Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910801341710336 |
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| author | Shang, Liang Sethares, William A. Adluru, Anusha Alexander, Andrew L. Prabhakaran, Vivek Nair, Veena A. Adluru, Nagesh |
| author_facet | Shang, Liang Sethares, William A. Adluru, Anusha Alexander, Andrew L. Prabhakaran, Vivek Nair, Veena A. Adluru, Nagesh |
| contents | Precise characterization of stroke lesions from MRI data has immense value in prognosticating clinical and cognitive outcomes following a stroke. Manual stroke lesion segmentation is time-consuming and requires the expertise of neurologists and neuroradiologists. Often, lesions are grossly characterized for their location and overall extent using bounding boxes without specific delineation of their boundaries. While such characterization provides some clinical value, to develop a precise mechanistic understanding of the impact of lesions on post-stroke vascular contributions to cognitive impairments and dementia (VCID), the stroke lesions need to be fully segmented with accurate boundaries. This work introduces the Multi-Stage Cross-Scale Attention (MSCSA) mechanism, applied to the U-Net family, to improve the mapping between brain structural features and lesions of varying sizes. Using the Anatomical Tracings of Lesions After Stroke (ATLAS) v2.0 dataset, MSCSA outperforms all baseline methods in both Dice and F1 scores on a subset focusing on small lesions, while maintaining competitive performance across the entire dataset. Notably, the ensemble strategy incorporating MSCSA achieves the highest scores for Dice and F1 on both the full dataset and the small lesion subset. These results demonstrate the effectiveness of MSCSA in segmenting small lesions and highlight its robustness across different training schemes for large stroke lesions. Our code is available at: https://github.com/nadluru/StrokeLesSeg. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15423 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention Shang, Liang Sethares, William A. Adluru, Anusha Alexander, Andrew L. Prabhakaran, Vivek Nair, Veena A. Adluru, Nagesh Image and Video Processing Computer Vision and Pattern Recognition Precise characterization of stroke lesions from MRI data has immense value in prognosticating clinical and cognitive outcomes following a stroke. Manual stroke lesion segmentation is time-consuming and requires the expertise of neurologists and neuroradiologists. Often, lesions are grossly characterized for their location and overall extent using bounding boxes without specific delineation of their boundaries. While such characterization provides some clinical value, to develop a precise mechanistic understanding of the impact of lesions on post-stroke vascular contributions to cognitive impairments and dementia (VCID), the stroke lesions need to be fully segmented with accurate boundaries. This work introduces the Multi-Stage Cross-Scale Attention (MSCSA) mechanism, applied to the U-Net family, to improve the mapping between brain structural features and lesions of varying sizes. Using the Anatomical Tracings of Lesions After Stroke (ATLAS) v2.0 dataset, MSCSA outperforms all baseline methods in both Dice and F1 scores on a subset focusing on small lesions, while maintaining competitive performance across the entire dataset. Notably, the ensemble strategy incorporating MSCSA achieves the highest scores for Dice and F1 on both the full dataset and the small lesion subset. These results demonstrate the effectiveness of MSCSA in segmenting small lesions and highlight its robustness across different training schemes for large stroke lesions. Our code is available at: https://github.com/nadluru/StrokeLesSeg. |
| title | Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.15423 |