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Training and assessing convolutional neural network performance in automatic vascular segmentation using Ga-68

R Parry1,2, K Wright3, J W Bellinge4,5

  • 1School of Medicine, The University of Western Australia, Perth, Australia. reece.parry@health.wa.gov.au.

The International Journal of Cardiovascular Imaging
|July 5, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence using nnU-Net accurately assesses vascular contours, calcification, and PET tracer uptake in Ga-68 DOTATATE PET/CT scans. This AI approach significantly reduces workflow time compared to manual segmentation.

Keywords:
Artificial intelligenceAutomatic segmentationCardiovascular inflammationCoronary artery diseaseDeep learningGallium-68 DOTATATE positron emission tomographyNeural network

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Nuclear Medicine

Background:

  • Ga-68 DOTATATE PET/CT is crucial for neuroendocrine tumor imaging.
  • Accurate assessment of vascular contours, calcification, and tracer uptake is vital for diagnosis and treatment monitoring.
  • Manual segmentation is time-consuming and subject to inter-observer variability.

Purpose of the Study:

  • To evaluate the performance of a convolutional neural network (nnU-Net) for segmenting vascular contours, calcification, and PET tracer activity in Ga-68 DOTATATE PET/CT.
  • To compare AI-driven segmentation with manual segmentation by an experienced observer.
  • To assess the impact of AI segmentation on workflow efficiency.

Main Methods:

  • A nnU-Net model was trained, validated, and tested on Ga-68 DOTATATE PET/CT scans from 116 patients.
  • Manual cardiac and aortic segmentations were performed by an experienced observer.
  • Comparisons were made between manual and AI segmentation for PET tracer uptake (SUVmean) and calcium scoring.

Main Results:

  • nnU-Net demonstrated strong positive correlations (r > 0.98) with manual segmentations for vascular contours.
  • No significant differences were observed in SUVmean values between manual and AI segmentation across various aortic segments.
  • Excellent agreement (r ≥ 0.80) was found between manual and AI measures for PET tracer uptake and vascular calcium scores.
  • AI segmentation significantly reduced workflow time compared to manual segmentation.

Conclusions:

  • nnU-Net provides accurate and reliable segmentation of vascular contours, calcification, and PET tracer uptake in Ga-68 DOTATATE PET/CT.
  • AI-driven segmentation offers comparable results to experienced observers while significantly improving workflow efficiency.
  • nnU-Net holds promise for enhancing the clinical utility of Ga-68 DOTATATE PET/CT in neuroendocrine tumor assessment.