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Ischemic Stroke l: Introduction

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Ischemic stroke is an acute cerebrovascular condition in which blood flow to a brain region is suddenly interrupted, leading to tissue infarction. Neurons depend on continuous oxygen and glucose supply, so even brief reductions in perfusion cause energy failure, ionic imbalance, and irreversible injury. Ischemic strokes are classified into thrombotic and embolic types based on their underlying mechanisms.Thrombotic MechanismsThrombotic stroke develops when a clot forms within a cerebral artery.

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FA-UNet: A FasterNet and Attention-Gated Hybrid Network for Precise Ischemic Stroke Segmentation.

Ishak Pacal^1,2, Ali Algarni³, Bilal Bayram⁴

¹Department of Computer Engineering, Faculty of Engineering, Igdir University, 76000 Igdir, Turkey.

Journal of Integrative Neuroscience

|November 7, 2025

Summary

This study introduces FA-UNet, a deep learning model for fast and accurate ischemic stroke lesion segmentation in diffusion-weighted imaging. FA-UNet achieves state-of-the-art results, improving clinical diagnosis and treatment planning.

Keywords:

U-Net deep learning diffusion-weighted imaging health image segmentation ischemic stroke

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Author Spotlight: Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT) for NonInvasive Whole-Brain Imaging of Ischemic Stroke

Author Spotlight: Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT) for NonInvasive Whole-Brain Imaging of Ischemic Stroke

Published on: June 2, 2023

Area of Science:

Medical Imaging
Artificial Intelligence
Neurology

Background:

Accurate segmentation of ischemic stroke lesions from diffusion-weighted imaging (DWI) is critical for patient care.
Manual segmentation is time-consuming and prone to variability.
Existing automated methods often face a trade-off between accuracy and computational efficiency.

Purpose of the Study:

To develop and validate a novel deep learning framework for automated ischemic stroke lesion segmentation.
To achieve high segmentation accuracy while maintaining computational efficiency for clinical application.

Main Methods:

Developed FasterNet and Attention-Gated UNet (FA-UNet), a hybrid U-Net architecture.
Incorporated a computationally efficient FasterNet block and multi-scale attention gates (MSAGs).
Trained and validated on the ISLES 2022 dataset and tested on 600 DWI scans from 80 patients.

Main Results:

FA-UNet achieved a Dice coefficient of 0.8676 and IoU of 0.7584 on the independent test set.
Outperformed existing state-of-the-art U-Net variants.
Demonstrated relative improvements of 1.64% in Dice score and 1.42% in IoU over the next best model.

Conclusions:

FA-UNet sets a new benchmark for automated ischemic stroke segmentation.
The model balances high accuracy with computational efficiency, offering a clinically viable tool.
Provides a robust and reliable solution for improving stroke diagnosis and treatment planning.