Automated Slice-Based Artery Identification in Various Field-of-View CTA Scans

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.