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Ischemic Stroke Lesion Core Segmentation from CT Perfusion Scans Using Attention ResUnet Deep Learning.
1College of Engineering and Technology, American University of the Middle East, Egaila, Kuwait. omar.alirr@aum.edu.kw.
Journal of Imaging Informatics in Medicine
|February 14, 2025
Summary
This study introduces a deep learning system for automated ischemic stroke lesion segmentation using Computed Tomography Perfusion (CTP) scans. The novel approach combines Edge Enhancing Diffusion filtering and an Attention ResUnet model, achieving 59% accuracy.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Neurology
Background:
- Accurate segmentation of ischemic stroke lesions is vital for patient diagnosis and treatment.
- Manual segmentation is labor-intensive and difficult in emergency situations.
- Automated methods are needed to improve efficiency and accuracy in clinical practice.
Purpose of the Study:
- To develop and evaluate a deep learning-based system for automated segmentation of ischemic stroke lesions from Computed Tomography Perfusion (CTP) data.
- To assess the effectiveness of integrating Edge Enhancing Diffusion (EED) filtering and an Attention ResUnet (AttResUnet) architecture for this task.
Main Methods:
- The proposed system utilizes Edge Enhancing Diffusion (EED) filtering for preprocessing CTP images, emphasizing lesion areas.
- An Attention ResUnet (AttResUnet) model with modified decoder incorporating spatial and channel attention mechanisms was employed for segmentation.
- The system was validated on the ISLES 2018 challenge dataset using a fivefold cross-validation strategy.
Main Results:
- The deep learning system achieved an average Dice Similarity Coefficient (DSC) of 59% for ischemic stroke lesion segmentation.
- Consistent performance was observed across different data subsets during the fold-wise analysis, indicating generalizability.
- The combination of EED filtering and attention mechanisms in the AttResUnet architecture proved effective for accurate segmentation.
Conclusions:
- The developed deep learning system provides a reliable and generalizable tool for automated ischemic stroke lesion segmentation from CTP datasets.
- This automated approach has the potential to enhance efficiency and diagnostic accuracy in clinical settings for stroke management.
- The integration of advanced deep learning techniques, including attention mechanisms, shows promise for improving medical image analysis in neurology.
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