Automated Slice-Based Artery Identification in Various Field-of-View CTA Scans
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Date
2015
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Automated identification of main arteries in Computed Tomography Angiography (CTA) scans plays a key role in the initialization of vessel tracking algorithms. Automated vessel tracking tools support physicians in vessel analysis and make their workflow time-efficient. We present a fully-automated framework for identification of five main arteries of three different body regions in various field-of-view CTA scans. Our method detects the two common iliac arteries, the aorta and the two common carotid arteries and delivers seed positions in them. After the field-of-view of a CTA scan is identified, artery candidate positions are regressed slice-wise and the best candidates are selected by Naive Bayes classification. Final artery seed positions are detected by picking the most optimal path over the artery classification results from slice to slice. Our method was evaluated on 20 CTA scans with various field-of-views. The high detection performance on different arteries shows its generality and future applicability for automated vessel analysis systems.
Description
@inproceedings{10.2312:vcbm.20151215,
booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},
editor = {Katja Bühler and Lars Linsen and Nigel W. John},
title = {{Automated Slice-Based Artery Identification in Various Field-of-View CTA Scans}},
author = {Major, David and Novikov, Alexey A. and Wimmer, Maria and Hladuvka, Jiri and Bühler, Katja},
year = {2015},
publisher = {The Eurographics Association},
ISSN = {2070-5786},
ISBN = {978-3-905674-82-8},
DOI = {10.2312/vcbm.20151215}
}