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Updated: Jun 27, 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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Stroke Lesion Segmentation and Deep Learning: A Comprehensive Review.

Mishaim Malik¹, Benjamin Chong^1,2,3, Justin Fernandez^1,3,4

¹Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand.

Bioengineering (Basel, Switzerland)

|January 22, 2024

Summary

This summary is machine-generated.

Deep learning models significantly improve stroke lesion segmentation, aiding in diagnosis and treatment. This review explores various models and preprocessing impacts for better stroke lesion analysis.

Keywords:

deep learning lesion segmentation network stroke

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Area of Science:

Neurology
Medical Imaging
Artificial Intelligence

Background:

Stroke impacts millions globally, causing significant motor, speech, cognitive, and emotional impairments.
Accurate stroke lesion segmentation is crucial for understanding anatomical information and patient prognosis.
Traditional manual segmentation methods are being surpassed by advanced computational techniques.

Purpose of the Study:

To review state-of-the-art deep learning models for stroke lesion segmentation.
To analyze the influence of various preprocessing techniques on model performance.
To guide future research in developing more effective stroke lesion segmentation tools.

Main Methods:

Comprehensive literature review of deep learning-based stroke lesion segmentation models.
Analysis of studies evaluating the impact of preprocessing techniques on segmentation accuracy.
Synthesis of findings to provide an overview of current methodologies.

Main Results:

Deep learning models demonstrate high efficacy in automated stroke lesion segmentation.
Preprocessing techniques significantly affect the performance and robustness of these models.
A variety of deep learning architectures are being applied with varying degrees of success.

Conclusions:

Deep learning offers a powerful approach to stroke lesion segmentation, improving efficiency and accuracy.
Optimizing preprocessing steps is essential for maximizing the performance of deep learning models.
Further research is needed to develop more robust and generalizable models for clinical application.