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Cerebral Artery and Vein Segmentation in Four-dimensional CT Angiography Using Convolutional Neural Networks.

Midas Meijs1, Sjoert A H Pegge1, Maria H E Vos1

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A deep learning model accurately segments arteries and veins in four-dimensional (4D) CT angiography scans in under 90 seconds, aiding in the diagnosis of acute ischemic stroke.

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Area of Science:

  • Medical imaging analysis
  • Deep learning in radiology
  • Cerebrovascular imaging

Background:

  • Accurate segmentation of cerebral vasculature is crucial for diagnosing cerebrovascular diseases like acute ischemic stroke.
  • Four-dimensional (4D) CT angiography provides dynamic imaging of blood flow but segmentation of arteries and veins can be time-consuming.

Purpose of the Study:

  • To develop and evaluate a deep learning approach for automated segmentation of arterial and venous cerebral vasculature using 4D CT angiography.
  • To assess the accuracy and efficiency of the deep learning model compared to manual segmentation.

Main Methods:

  • A three-dimensional Dense-U-Net model was trained using manually annotated cerebral artery and vein maps from 60 patients.
  • Input data included weighted temporal average and variance from 4D CT angiography scans.
  • The model was evaluated quantitatively on 40 patients and qualitatively on 277 patients.

Main Results:

  • The deep learning model achieved a mean Dice similarity coefficient of 0.80 for arteries and 0.88 for veins.
  • Mean relative absolute volume differences were 7.3% for arteries and 8.5% for veins.
  • Automated segmentation processing time was less than 90 seconds per patient, with 99.3% of segmentations rated as very good to perfect.

Conclusions:

  • The proposed convolutional neural network provides accurate and rapid segmentation of cerebral arteries and veins from 4D CT angiography.
  • This deep learning approach has the potential to significantly improve the efficiency of cerebrovascular imaging analysis for conditions such as acute ischemic stroke.