title: Online artery diameter measurement in ultrasound images using artificial neural networks.
abstract: An automated online technique is described for measurement of artery diameter in flow-mediated dilation (FMD) ultrasound (US) images, using artificial neural networks to identify and track artery walls. This allows FMD results to be calculated without the inherent delay of current retrospective methods. Two networks were trained to identify artery anterior and posterior walls using over 3200 examples from carotid artery images. Both networks correctly classified approximately 97% of the randomly selected test samples. The technique was verified using a physical model with absolute measurement error of -1.16% +/- 1.04% (mean +/- SD) over the diameter range 2 to 8 mm. Advantages of the technique include: online analysis; wall tracking optimisation before the study proper; measurement of diameter changes over the cardiac cycle; low FMD measurement variance; minimal image degradation; and no unwieldy image store. Measurement of artery diameter changes over the cardiac cycle was explored using simulated image sequences generated with a virtual US scanner.
authors.affiliation: Department of Medical Physics and Bioengineering, St. George's Hospital, Blackshaw Road, Tooting, London, UK. val@stgmic.demon.co.uk
authors.forename: V R
authors.initials: VR
authors.lastname: Newey
authors.forename: D K
authors.initials: DK
authors.lastname: Nassiri
mesh_terms: D002339:Carotid Arteries / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D016571:Neural Networks, Computer; D009862:Online Systems; D014463:Ultrasonography