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Related Experiment Video

Updated: May 5, 2026

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

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Segmentation of stroke lesions using transformers-augmented MRI analysis.

Ramsha Ahmed¹, Aamna Al Shehhi^1,2, Naoufel Werghi³

¹Department of Biomedical Engineering and Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, UAE.

Human Brain Mapping

|August 9, 2024

Summary

This study introduces a novel deep learning method combining transformers and data augmentation for accurate chronic stroke lesion segmentation in MRI scans. The approach significantly improves lesion delineation, outperforming existing methods on benchmark datasets.

Keywords:

MRI brain lesions chronic stroke data augmentation deep learning lesion segmentation transformers

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Published on: September 25, 2019

Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Medicine
Neuroscience

Background:

Chronic stroke lesion segmentation from MRI is challenging due to lesion heterogeneity.
Existing machine learning methods show moderate performance in delineating these lesions.

Purpose of the Study:

To develop an accurate and generalizable method for chronic stroke lesion segmentation.
To improve upon existing machine learning techniques for lesion delineation.

Main Methods:

Integration of transformers' deformable feature attention with convolutional deep learning.
Implementation of an ecological data augmentation technique by inserting real lesions into healthy brain regions.

Main Results:

Achieved a Dice index of 0.82 (±0.39) on the ATLAS 2022 dataset, outperforming existing methods.
Demonstrated robust performance, particularly for small stroke lesions.
Validated on the ISLES 2015 dataset, showing effectiveness on unseen brain scans.

Conclusions:

The proposed method, combining transformers and ecological data augmentation, offers a robust approach for chronic stroke lesion segmentation.
This technique achieves clinically relevant accuracy and can be extended to segment other brain abnormalities.