STROKE LESION SEGMENTATION USING MULTI-STAGE CROSS-SCALE ATTENTION.

Area of Science:

• Medical imaging analysis
• Artificial intelligence in neurology
• Neuroscience

Background:

• Accurate stroke lesion segmentation from MRI is crucial for predicting patient outcomes.
• Manual segmentation is labor-intensive and requires specialized expertise.
• Current methods often lack precision, especially for small lesions, hindering mechanistic understanding of post-stroke cognitive impairment.

Purpose of the Study:

• To develop and evaluate an automated method for precise stroke lesion segmentation.
• To improve the characterization of lesions, particularly small ones, for better understanding of their impact on cognitive function.
• To introduce the Multi-Stage Cross-Scale Attention (MSCSA) mechanism for enhanced MRI lesion segmentation.

Main Methods:

• Application of the Multi-Stage Cross-Scale Attention (MSCSA) mechanism within the U-Net deep learning architecture.
• Utilizing the Anatomical Tracings of Lesions After Stroke (ATLAS) v2.0 dataset for training and validation.
• Comparative analysis against baseline methods using Dice and F1 scores, with a focus on small lesion segmentation.

Main Results:

• MSCSA demonstrated superior performance in segmenting small stroke lesions compared to baseline methods, achieving high Dice and F1 scores.
• An ensemble strategy incorporating MSCSA yielded the highest scores on both the full dataset and the small lesion subset.
• The method showed robustness across different training schemes for segmenting both small and large stroke lesions.

Conclusions:

• The MSCSA mechanism is effective for accurate and automated segmentation of stroke lesions, especially small ones.
• This improved segmentation capability can enhance the understanding of vascular contributions to cognitive impairments and dementia (VCID) post-stroke.
• The developed approach offers a promising tool for clinical research and potentially for patient care.