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Infarct core segmentation using U-Net in CT perfusion imaging: a feasibility study.

Ching-Ching Yang1,2, Shih-Sheng Chen3

  • 1Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan.

Acta Radiologica (Stockholm, Sweden : 1987)
|January 23, 2025
PubMed
Summary
This summary is machine-generated.

This study shows U-Net can accurately segment infarct core in CT perfusion imaging, especially for larger stroke areas. This AI tool may help identify patients suitable for intravenous thrombolysis.

Keywords:
ISLESInfarct core segmentationcomputed tomography perfusion imagingdeep learningparametric maps

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neurology

Background:

  • Computed Tomography (CT) perfusion imaging has variable thresholds for infarct core measurement, causing debate in stroke diagnosis.
  • Accurate infarct core segmentation is crucial for effective stroke treatment decisions.

Purpose of the Study:

  • To evaluate the feasibility of using a U-Net model for automated infarct core segmentation in CT perfusion imaging.
  • To compare the performance of U-Net with different CT perfusion parameters as input.

Main Methods:

  • U-Net model was trained using CT perfusion parametric maps as input.
  • Ground truth segmentation was established using diffusion-weighted imaging (DWI) from the ISLES2018 dataset.
  • Segmentation accuracy was quantified using Dice Similarity Coefficient (DSC), sensitivity, and Intersection over Union (IoU).

Main Results:

  • U-Net achieved the highest DSC when using Mean Transit Time (MTT) or Time-to-Maximum (Tmax) as input.
  • Tmax input yielded the highest sensitivity and IoU.
  • DSC improved with larger infarct areas, reaching 0.6-0.8 for areas ≥2000 pixels.

Conclusions:

  • The U-Net model demonstrates robust performance in segmenting larger infarct cores (≥2000 pixels) in CT perfusion imaging.
  • This AI-driven approach shows potential for aiding in the identification of patients eligible for intravenous thrombolysis.