We introduce a framework for quantitative evaluation of 3D vessel segmentation approaches using vascular phantoms. Phantoms are designed using a CAD system and created with a 3D printer, and comprise realistic shapes including branches and pathologies such as abdominal aortic aneurysms (AAA). To transfer ground truth information to the 3D image coordinate system, we use a landmark-based registration scheme utilizing fiducial markers integrated in the phantom design. For accurate 3D localization of the markers we developed a novel 3D parametric intensity model that is directly fitted to the markers in the images. We also performed a quantitative evaluation of different vessel segmentation approaches for a phantom of an AAA.
This paper introduces a new approach to segment vessels from medical images using the fast marching method.
Our approach relies on an iterative scheme: Starting from a given start point and initial direction, the optimal
path within a circular region of interest (ROI) around this point is found using the fast marching method and a
combination of different speed functions. Besides using speed functions based on a vesselness measure and the
vessel radius, we introduce a directional speed function which prefers directions close to the predicted direction.
The end point of the detected path is then used as the new start point to find again the optimal path within a
new ROI centered around this point. This procedure is repeated until the user-specified end point is reached,
or some other termination criterion is satisfied. The final result is the concatenation of the sequence of paths of
the individual ROIs. Our approach has been applied to synthetic and real datasets. The experiments show that
our approach is not only more efficient than a previous fast marching approach but also produces better results
when dealing with short cuts and crossings in the segmentation of long vessels.