Related Experiment Video
Updated: Sep 10, 2025
10:25
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
Published on: September 25, 2019
48.4K
Enhanced Brain Stroke Lesion Segmentation in MRI Using a 2.5D Transformer Backbone U-Net Model.
Mahsa Karimzadeh1, Hadi Seyedarabi1, Ata Jodeiri2
1Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666, Iran.
Brain Sciences
|August 28, 2025
Summary
This study introduces an improved U-Net deep learning model with a transformer backbone for accurate brain stroke lesion segmentation. The novel approach significantly enhances diagnostic tools for timely clinical intervention.
Area of Science:
- Medical Image Analysis
- Deep Learning
- Neuroimaging
Background:
- Accurate segmentation of brain stroke lesions from MRI is crucial for diagnosis and treatment planning.
- Existing methods face challenges in balancing accuracy and computational complexity.
Purpose of the Study:
- To develop and evaluate a novel deep learning model for precise brain stroke lesion segmentation.
- To enhance the U-Net architecture with a transformer-based backbone and a 2.5D approach.
Main Methods:
- Implemented a U-Net model with a Mix Vision Transformer (MiT) backbone.
- Utilized a 2.5D method for processing 3D MRI data slices.
- Evaluated performance on the 2015 ISLES dataset using 4-fold cross-validation.
Main Results:
- The proposed U-Net with MiT backbone and 2.5D method achieved superior performance.
- Achieved Dice Coefficient of 0.8153 ± 0.0101 and IoU of 0.7835 ± 0.0079.
- Outperformed other state-of-the-art models including CNN-based UNet, nnU-Net, TransUNet, and SwinUNet.
Conclusions:
- Integrating transformer backbones and 2.5D techniques significantly advances brain stroke lesion segmentation.
- The developed model offers a more reliable and efficient tool for clinical diagnostic applications.
