title: Automatic detection of carotid arteries in computed tomography angiography: a proof of concept protocol.
abstract: Atherosclerosis is one of the leading causes of mortality in the western world. Computed tomography angiography (CTA) is the conventional imaging method used for pre-surgery assessment of the blood flow within the carotid vessel. In this paper, we present a proof of concept of a novel, fast and operator independent protocol for the automatic detection (seeding) of the carotid arteries in CTA in the thorax and upper neck region. The dataset is composed of 14 patients' CTA images of the neck region. The performance of this method is compared with manual seeding by four trained operators. Inter-operator variation is also assessed based on the dataset. The minimum, average and maximum coefficient of variation among the operators was (0, 2, 5 %), respectively. The performance of our method is comparable with the state of the art alternative, presenting a detection rate of 75 and 71 % for the lowest and uppermost image levels, respectively. The mean processing time is 167 s per patient versus 386 s for manual seeding. There are no significant differences between the manual and automatic seed positions in the volumes (p = 0.29). A fast, operator independent protocol was developed for the automatic detection of carotid arteries in CTA. The results are encouraging and provide the basis for the creation of automatic detection and analysis tools for carotid arteries.
authors.affiliation: Department of Electronics and Communications Engineering, Tampere University of Technology, Finn-Medi 1 4-208, Biokatu 6, 33520, Tampere, Finland. florentino.caetanodossantos@tut.fi.
authors.forename: Florentino Luciano Caetano
authors.identifier: http://orcid.org/0000-0001-8151-3585
authors.initials: FL
authors.lastname: Dos Santos
authors.affiliation: Department of Electronics and Communications Engineering, Tampere University of Technology, Finn-Medi 1 4-208, Biokatu 6, 33520, Tampere, Finland.
authors.forename: Atte
authors.initials: A
authors.lastname: Joutsen
authors.affiliation: Department of Electronics and Communications Engineering, Tampere University of Technology, Finn-Medi 1 4-208, Biokatu 6, 33520, Tampere, Finland.|BioMediTech, Tampere, Finland.
authors.forename: Michelangelo
authors.initials: M
authors.lastname: Paci
authors.affiliation: Division of Vascular Surgery, Department of Surgery, Tampere University Hospital and Medical School, Tampere, Finland.
authors.forename: Juha
authors.initials: J
authors.lastname: Salenius
authors.affiliation: Department of Electronics and Communications Engineering, Tampere University of Technology, Finn-Medi 1 4-208, Biokatu 6, 33520, Tampere, Finland.|Department of Radiology, Regional Imaging Centre, Tampere University Hospital, Tampere, Finland.
authors.forename: Hannu
authors.initials: H
authors.lastname: Eskola
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001331:Automation; D002339:Carotid Arteries / Q000000981:diagnostic imaging* / Q000503:physiopathology; D016893:Carotid Stenosis / Q000000981:diagnostic imaging* / Q000503:physiopathology; D000072226:Computed Tomography Angiography; D003287:Contrast Media / Q000008:administration & dosage; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D061330:Multidetector Computed Tomography; D015588:Observer Variation; D011237:Predictive Value of Tests; D011857:Radiographic Image Interpretation, Computer-Assisted; D012039:Regional Blood Flow; D015203:Reproducibility of Results; D012720:Severity of Illness Index; D012984:Software
chemical_list: D003287:Contrast Media
keywords: Atherosclerosis; Automatic image analysis; Carotid angiography; Machine vision