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Related Experiment Video

Updated: Jun 14, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
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Segmentation of Leukoaraiosis on Noncontrast Head CT Using CT-MRI Paired Data Without Human Annotation.

Wi-Sun Ryu1, Jae W Song2, Jae-Sung Lim3

  • 1Artificial Intelligence Research Center, JLK Inc., Seoul, Republic of Korea.

Brain and Behavior
|June 11, 2025
PubMed
Summary

A new deep learning algorithm accurately segments leukoaraiosis (LA) on CT scans, improving assessments for ischemic stroke patients by correlating well with MRI data and clinical outcomes.

Keywords:
computed tomographydeep learningleukoaraiosismagnetic resonance imagingsegmentation algorithmwhite matter hyperintensities

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Neurology

Background:

  • Leukoaraiosis (LA) evaluation on CT is difficult due to low contrast and resemblance to gliosis.
  • Accurate LA quantification is crucial for ischemic stroke patient management.

Purpose of the Study:

  • To develop and validate a deep learning algorithm for segmenting LA on CT.
  • To assess the algorithm's performance against MRI-derived labels and its clinical utility.

Main Methods:

  • A deep learning model (2D nnU-Net) was trained on CT-MRIFLAIR paired data from a Korean registry.
  • Pseudo-ground-truth LA labels were generated on CT via deformable image registration.
  • Performance was validated using Dice similarity coefficient (DSC), concordance correlation coefficient (CCC), and Pearson correlation in internal, external, and US cohorts.

Main Results:

  • The algorithm achieved a DSC of 0.527 in the external test set.
  • Predicted LA volumes strongly correlated with registered LA (r=0.953) and WMH (r=0.951).
  • LA volume showed significant associations with age, risk factors, and 3-month poststroke outcomes.

Conclusions:

  • The developed deep learning algorithm provides a reproducible method for LA segmentation on CT.
  • This approach bridges the gap between CT and MRI assessments in ischemic stroke patients.