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Computer-Aided Evaluation of Blood Vessel Geometry From Acoustic Images.

Stefan B LindströmFredrik UhlinNiclas BjarnegårdMicael GyllingKamilla NilssonChristina SvenssonPia Yngman-UhlinToste Länne
Published in: Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine (2017)
A method for computer-aided assessment of blood vessel geometries based on shape-fitting algorithms from metric vision was evaluated. Acoustic images of cross sections of the radial artery and cephalic vein were acquired, and medical practitioners used a computer application to measure the wall thickness and nominal diameter of these blood vessels with a caliper method and the shape-fitting method. The methods performed equally well for wall thickness measurements. The shape-fitting method was preferable for measuring the diameter, since it reduced systematic errors by up to 63% in the case of the cephalic vein because of its eccentricity.
Keyphrases
  • deep learning
  • optical coherence tomography
  • convolutional neural network
  • machine learning
  • optic nerve
  • healthcare
  • primary care
  • emergency department
  • patient safety
  • general practice
  • adverse drug
  • clinical evaluation