This work presents novel methods to accurately placing landmarks inside the vessel lumen. This task is an
important prerequisite to automatic centerline tracing. Methods have been proposed in the past to determine the
location of organ landmarks, and yet several challenges remain for vascular landmarks. First, placing landmarks
inside the lumen could be challenging for narrow vessels. Second, contrast-enhanced arteries could be tightly
surrounded by bones with similar intensity profiles, making detection difficult compared to arteries surrounded
only by darker tissues. Third, landmarks not located at bifurcations could be ill-defined as they have high
uncertainty in position.
We first present a method to detect landmarks that are located at vessel bifurcations. Such landmarks have
well-defined positions, and we detect them using machine learning techniques. We then present a method to
detect vascular landmarks not located at bifurcations. First, a segment detector is created to detect a vessel
segment. Annotating multiple points along a vessel segment is easier than annotating a single landmark position,
as there is no well-defined position along a vessel. This resolves the ambiguity issue mentioned above. Second,
spatial features are computed from the segment detector's response map, and a regression model is created which
takes as input the local spatial features surrounding a voxel, and outputs a confidence score of how likely this
voxel is inside the lumen. We evaluate the system on a set of 94 3D CT datasets.