Endovascular imaging aims at identifying vessels and their branches. Automatic vessel segmentation and bifurcation
detection eases both clinical research and routine work. In this article a state of the art bifurcation
detection algorithm is developed and applied on vascular computed tomography angiography (CTA) scans to
mark the common iliac artery and its branches, the internal and external iliacs.
In contrast to other methods our algorithm does not rely on a complete segmentation of a vessel in the
3D volume, but evaluates the cross-sections of the vessel slice by slice. Candidates for vessels are obtained by
thresholding, following by 2D connected component labeling and prefiltering by size and position. The remaining
candidates are connected in a squared distanced weighted graph. With Dijkstra algorithm the graph is traversed
to get candidates for the arteries. We use another set of features considering length and shape of the paths to
determine the best candidate and detect the bifurcation.
The method was tested on 119 datasets acquired with different CT scanners and varying protocols. Both
easy to evaluate datasets with high resolution and no apparent clinical diseases and difficult ones with low
resolution, major calcifications, stents or poor contrast between the vessel and surrounding tissue were included.
The presented results are promising, in 75.7% of the cases the bifurcation was labeled correctly, and in 82.7% the
common artery and one of its branches were assigned correctly. The computation time was on average 0.49 s ±
0.28 s, close to human interaction time, which makes the algorithm applicable for time-critical applications.
Automatic examination of medical images becomes increasingly important due to the rising amount of data.
Therefore automated methods are required which combine anatomical knowledge and robust segmentation to
examine the structure of interest. We propose a statistical model of the vascular tree based on vascular landmarks
and unbranched vessel sections. An undirected graph provides anatomical topology, semantics, existing landmarks
and attached vessel sections. The atlas was built using semi-automatically generated geometric models of
various body regions ranging from carotid arteries to the lower legs. Geometric models contain vessel centerlines
as well as orthogonal cross-sections in equidistant intervals with the vessel contour having the form of a polygon
path. The geometric vascular model is supplemented by anatomical landmarks which are not necessarily related
to the vascular system. These anatomical landmarks define point correspondences which are used for registration
with a Thin-Plate-Spline interpolation. After the registration process, the models were merged to form
the statistical model which can be mapped to unseen images based on a subset of anatomical landmarks. This
approach provides probability distributions for the location of landmarks, vessel-specific geometric properties
including shape, expected radii and branching points and vascular topology. The applications of this statistical
model include model-based extraction of the vascular tree which greatly benefits from vessel-specific geometry
description and variation ranges. Furthermore, the statistical model can be applied as a basis for computer aided
diagnosis systems as indicator for pathologically deformed vessels and the interaction with the geometric model
is significantly more user friendly for physicians through anatomical names.
The detection of anatomical landmarks is an important prerequisite to analyze medical images fully automatically.
Several machine learning approaches have been proposed to parse 3D CT datasets and to determine the
location of landmarks with associated uncertainty. However, it is a challenging task to incorporate high-level
anatomical knowledge to improve these classification results. We propose a new approach to validate candidates
for vessel bifurcation landmarks which is also applied to systematically search missed and to validate ambiguous
landmarks. A knowledge base is trained providing human-readable geometric information of the vascular system,
mainly vessel lengths, radii and curvature information, for validation of landmarks and to guide the search
process. To analyze the bifurcation area surrounding a vessel landmark of interest, a new approach is proposed
which is based on Fast Marching and incorporates anatomical information from the knowledge base. Using the
proposed algorithms, an anatomical knowledge base has been generated based on 90 manually annotated CT
images containing different parts of the body. To evaluate the landmark validation a set of 50 carotid datasets
has been tested in combination with a state of the art landmark detector with excellent results. Beside the
carotid bifurcation the algorithm is designed to handle a wide range of vascular landmarks, e.g. celiac, superior
mesenteric, renal, aortic, iliac and femoral bifurcation.
Extracting the centerline of blood vessels is a frequently used technique to assist the physician in the diagnosis
of common artery disease in CTA images. Thereby, a robust and precise computation of the centerline is an
essential prerequisite. In this paper we present a novel approach to robustly model the vessel tree and to compute
its centerline. The algorithm is initialized with two clicks from the physician, which mark the start and end point
of the vessel to be examined. Our approach is divided into two consecutive steps. In the first step, a section of the
vessel tree is mapped to the model so that the desired centerline is entirely included. After the generation of the
model, the centerline can easily be extracted in the second step. The robust and efficient extraction of required
model parameters is performed by a ray-casting approach. The proposed method determines a set of points on
the vascular wall. The analysis of these points using the principal component analysis provides all parameters
needed for modeling the vessel. The proposed technique reduces computation time and does not require a
segmentation of the vessel lumen to determine the centerline of the vessel. Furthermore, a priori knowledge of
vessel structures is incorporated to improve robustness in the presence of pathological deformations.
For assessment of coronary artery disease (CAD) and peripheral artery disease (PAD) the automatic extraction
of vessel centerlines is a crucial technology. In the most common approach two seed points have to be manually
placed in the vessel and the centerline is automatically computed between these points. This methodology is
appropriate for the quantitative analysis of single vessel segments. However, for an interactive and fast reading
of complete datasets a more interactive approach would be beneficial.
In this work we introduce an interactive vessel-tracking approach which eases the reading of cardiac and
vascular CTA datasets. Starting with a single seed point a local vessel-tracking is initialized and extended in
both directions while the user "walks" along the vessel centerline. For a robust tracking of a wide variety of vessel
diameters, from coronaries to the aorta, we combine a local A*-graph-search for tiny vessels and a model-based
tracking for larger vessels to an hybrid model-based and graph-based approach.
In order to further ease the reading of cardiac and vascular CTA datasets, we introduce a subdivision of the
interactively acquired centerline into segments that can be approximated by a single plane. This subdivision
allows the visualization of the vessel in optimally oriented multi-planar reformations (MPR). The proposed
visualization combines the advantage of a curved planar reformation (CPR), showing a large part of the vessel
in one view, with the benefits of a MPR, having a non distorted more trustable image.