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Non-invasive Imaging and Analysis of Cerebral Ischemia in Living Rats Using Positron Emission Tomography with 18F-FDG

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Ischemic Stroke Lesion Core Segmentation from CT Perfusion Scans Using Attention ResUnet Deep Learning.

Omar Ibrahim Alirr¹

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 summary is machine-generated.

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.

Keywords:

Attention U-Net CT perfusion EED Ischemic stroke Segmentation

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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.