Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention

TL;DR

This paper introduces a multi-stage cross-scale attention mechanism integrated into U-Net for improved stroke lesion segmentation in MRI, especially excelling at small lesion detection and boundary delineation.

Contribution

The novel MSCSA mechanism enhances the segmentation of stroke lesions, particularly small ones, outperforming baseline methods on the ATLAS v2.0 dataset.

Findings

MSCSA outperforms baseline methods in Dice and F1 scores.

Ensemble with MSCSA achieves highest segmentation accuracy.

Effective in segmenting small and large stroke lesions.

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.