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Related Experiment Video

Updated: May 28, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

White Matter Infarct Detection with Transformer and Auto-ML-Derived Models.

Vitaly Dobromyslin1, Wenjin Zhou2

  • 1Francis College of Engineering, University of Massachusetts, Lowell, MA 01854, USA.

Brain Sciences
|May 27, 2026
PubMed
Summary

Stroke detection using T1-w imaging shows dataset drift. Novel resting-state fMRI biomarkers improve stroke detection and predict recovery, enhancing patient outcomes.

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Area of Science:

  • Neuroimaging
  • Medical Artificial Intelligence
  • Stroke Medicine

Background:

  • Stroke mortality rates are rising after a decline.
  • Early stroke detection is crucial for treatment and preventing further infarcts.
  • Current imaging lacks comprehensive acute and chronic stroke detection capabilities.

Purpose of the Study:

  • To develop novel imaging biomarkers for stroke detection and prognosis.
  • To evaluate a U-shaped, nested hierarchical transformer model (UNesT) for stroke segmentation.
  • To identify new biomarkers from resting-state fMRI (rs-fMRI) to improve stroke detection.

Main Methods:

  • Trained a UNesT model for T1-weighted white matter infarct segmentation on the ATLAS R2 dataset.
  • Evaluated model reproducibility on the independent Washington University (WU) stroke dataset.
Keywords:
auto-MLlesion segmentationrs-fMRI biomarkerstroke detectionstroke recovery prognosisvision transformer

Related Experiment Videos

Last Updated: May 28, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

  • Utilized automated machine learning to extract 77 rs-fMRI biomarkers to enhance UNesT performance.

    • Main Results:

    • T1-w UNesT model performance decreased significantly on the WU dataset (Dice indices 0.24-0.41).
    • Re-optimization on the WU dataset improved test set Dice index to 0.41-0.50.
    • Spectral peak amplitude from rs-fMRI improved T1-w UNesT Dice index (0.41 to 0.50, p < 0.01) and correlated with language recovery.

    Conclusions:

    • Model performance is sensitive to dataset drift, necessitating careful validation.
    • Spectral peak amplitude emerges as a promising rs-fMRI biomarker for stroke detection.
    • This rs-fMRI biomarker aids in predicting stroke recovery trajectories.