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Optimized Management of Endovascular Treatment for Acute Ischemic Stroke

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Better Diffusion Segmentation in Acute Ischemic Stroke Through Automatic Tree Learning Anomaly Segmentation.

Jens K Boldsen¹, Thorbjørn S Engedal¹, Salvador Pedraza²

¹Department of Clinical Medicine, Center of Functional Integrative Neuroscience, Aarhus University, Aarhus, Denmark.

Frontiers in Neuroinformatics

|June 19, 2018

Summary

This summary is machine-generated.

A new algorithm, ATLAS, accurately delineates ischemic stroke core lesions for faster patient triage. This machine learning tool improves upon existing methods, aiding in timely treatment decisions for better patient outcomes.

Keywords:

computer learning decision trees diffusion MRI diffusion lesion segmentation stroke

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Area of Science:

Neurology
Medical Imaging
Artificial Intelligence

Background:

Stroke is a leading cause of death and disability worldwide, with ischemic strokes accounting for the majority.
Prompt assessment of irreversible brain tissue damage (core lesion) is crucial for effective acute stroke management.
Magnetic Resonance Imaging (MRI), specifically diffusion-weighted imaging, is vital for quantifying lesion volume.

Purpose of the Study:

To introduce ATLAS, a fully automated machine learning algorithm for rapid and standardized delineation of the ischemic stroke core lesion.
To compare the performance of ATLAS against established threshold-based methods and the state-of-the-art COMBAT Stroke algorithm.
To evaluate ATLAS's accuracy in quantifying lesion volume for improved patient triaging.

Main Methods:

ATLAS, a machine learning algorithm using decision trees and spatial regularization, was developed and trained on expert delineations.
The algorithm was tested on acute anterior circulation stroke patient data from the I-Know multicenter study using leave-one-out cross-validation.
Performance was quantified using the Dice index for lesion overlap and standard deviation of residuals for volume accuracy.

Main Results:

ATLAS achieved a significantly higher median Dice coefficient (0.6122) compared to COMBAT Stroke (0.5636) and threshold-based methods (0.3951-0.2839).
The ATLAS algorithm demonstrated superior accuracy in volume quantification, with a lower standard deviation of residuals (10.25 ml) versus COMBAT Stroke (17.53 ml).
Leave-one-out cross-validation confirmed robust performance on independent patient data.

Conclusions:

ATLAS provides a highly accurate and automated solution for delineating ischemic stroke core lesions.
The algorithm's superior performance in lesion segmentation and volume quantification can enhance clinical decision-making.
ATLAS has the potential to optimize patient triaging for timely and appropriate acute stroke therapies.