Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention
Journal:
arXiv
Published Date:
Jan 26, 2025
Abstract
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.
