In this paper, we present a fully automated approach to coronary vessel segmentation, which involves calcification or soft plaque delineation in addition to accurate lumen delineation, from 3D Cardiac Computed Tomography Angiography data. Adequately virtualizing the coronary lumen plays a crucial role for simulating blood ow by means of fluid dynamics while additionally identifying the outer vessel wall in the case of arteriosclerosis is a prerequisite for further plaque compartment analysis. Our method is a hybrid approach complementing Active Contour Model-based segmentation with an external image force that relies on a Random Forest Regression model generated off-line. The regression model provides a strong estimate of the distance to the true vessel surface for every surface candidate point taking into account 3D wavelet-encoded contextual image features, which are aligned with the current surface hypothesis. The associated external image force is integrated in the objective function of the active contour model, such that the overall segmentation approach benefits from the advantages associated with snakes and from the ones associated with machine learning-based regression alike. This yields an integrated approach achieving competitive results on a publicly available benchmark data collection (Rotterdam segmentation challenge).
KEYWORDS: Image segmentation, 3D modeling, 3D image processing, Arteries, Image processing algorithms and systems, Computed tomography, 3D applications, Roads, Medical imaging, Angiography
We present an efficient algorithm for the robust segmentation of vessel contours in Computed Tomography Angiography
(CTA) images. The algorithm performs its task within several steps based on a 3D Active Contour Model (ACM) with
refinements on Multi-Planar Reconstructions (MPRs) using 2D ACMs. To be able to distinguish true vessel edges from
spurious, an adaptive self-learning edge model is applied. We present details of the algorithm together with an evaluation
on n=150 CTA data sets and compare the results of the automatic segmentation with manually outlined contours
resulting in a median dice similarity coefficient (DSC) of 92.2%. The algorithm is able to render 100 contours within
1.1s on a Pentium®4 CPU 3.20 GHz, 2 GByte of RAM.
KEYWORDS: Image segmentation, 3D modeling, Visualization, 3D image processing, Opacity, Image visualization, Computed tomography, Visual process modeling, Metals, Binary data
In this paper we present an efficient algorithm for the segmentation of the inner and outer boundary of thoratic and abdominal aortic aneurysms (TAA & AAA) in computed tomography angiography (CTA) acquisitions. The aneurysm segmentation includes two steps: first, the inner boundary is segmented based on a grey level model with two thresholds; then, an adapted active contour model approach is applied to the more complicated outer boundary segmentation, with its
initialization based on the available inner boundary segmentation. An opacity image, which aims at enhancing important features while reducing spurious structures, is calculated from the CTA images and employed to guide the deformation of the model. In addition, the active contour model is extended by a constraint force that prevents intersections of the inner and outer boundary and keeps the outer boundary at a distance, given by the thrombus thickness, to the inner
boundary. Based upon the segmentation results, we can measure the aneurysm size at each centerline point on the centerline orthogonal multiplanar reformatting (MPR) plane. Furthermore, a 3D TAA or AAA model is reconstructed from the set of segmented contours, and the presence of endoleaks is detected and highlighted. The implemented method has been evaluated on nine clinical CTA data sets with variations in anatomy and location of the pathology and has
shown promising results.
In this paper a method is introduced, to visualize bifurcated stent grafts in CT-Data. The aim is to improve therapy
planning for minimal invasive treatment of abdominal aortic aneurysms (AAA). Due to precise measurement of the
abdominal aortic aneurysm and exact simulation of the bifurcated stent graft, physicians are supported in choosing a
suitable stent prior to an intervention. The presented method can be used to measure the dimensions of the abdominal
aortic aneurysm as well as simulate a bifurcated stent graft. Both of these procedures are based on a preceding
segmentation and skeletonization of the aortic, right and left iliac. Using these centerlines (aortic, right and left iliac) a
bifurcated initial stent is constructed. Through the implementation of an ACM method the initial stent is fit iteratively to
the vessel walls - due to the influence of external forces (distance- as well as balloonforce). Following the fitting
process, the crucial values for choosing a bifurcated stent graft are measured, e.g. aortic diameter, right and left common
iliac diameter, minimum diameter of distal neck. The selected stent is then simulated to the CT-Data - starting with the
initial stent. It hereby becomes apparent if the dimensions of the bifurcated stent graft are exact, i.e. the fitting to the
arteries was done properly and no ostium was covered.
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