Coronary Centerline Extraction Using Multiple Hypothesis Tracking and Minimal Paths

Abstract

This paper describes an interactive approach to the identification of coronary arteries in 3D angiography images. The approach is based on a novel multiple hypothesis tracking methodology which is complemented with a standard minimal path search, and it allows for a complete segmentation with little manual labor. When evaluated using the 3D CT angiography data supplied with the MICCAI'08 workshop 3D Segmentation in the Clinic: A Grand Challenge II, 98% of the target coronary arteries could be segmented in about 5 minutes per data set with the same spatial accuracy achieved in manual segmentations by human experts.