<title>Computer-assisted diagnosis in CT angiography of abdominal aortic aneurysms</title>

Abstract

The purpose ofthis study was to develop methods for automatic 3D-segmentation and automatic quantification of vascular structures in CT angiographic studies, e.g., abdominal aortic aneurysms. Methods for segmentation were developed based on thresholding, maximum gradient, and second derivative techniques. All parameters for the segmentation are generated automatically, i.e. no user interaction is necessary for this process. Median filtering of all images is initially performed to reduce the image noise. The algorithm then automatically identifies the starting point inside the aorta for the volume growing. The segmentation of the vascular tree is achieved in two. steps. First, only the aorta and small parts of branch vessels are segmented by using strong restrictions in the parameters for threshold and gradient. A description of the aorta is generated by fitting the detected outer border of the aorta with an ellipse. This description includes centerline, direction, contour, eccentricity, and area. In the second step, segmentation parameters are changed automatically for segmentation of branch vessels. A shaded surface display of the segmented structures is then generated.The segmentation of the aorta appears accurate, is fast, and the 3D display can be manipulated in real time. The quantitative description of the aorta is reliable giving reproducible information. Total CPU time for the segmentation and description is less than five minutes on a standard workstation. Time-consuming manual segmentation and parameterization of vascular structures are obviated, with 3D visualization and quantitative results available in minutes instead of hours. This technique for segmentation and description of the aorta and renal arteries shows the feasibility of computer assisted diagnosis in CT angiographic studies without user interaction. Besides the description, a rapid 3D view of the vessels is generated, often needed by the physician and normally only achievable by time consuming manual segmentation.

Experimental comparison of cone beam CT (CBCT) reconstruction and multiview reconstruction (MVR) for microangiography (MA) detector system

“…A 3D rendering can be made from a 3D centerline that can be calculated from the micro-CBCT centerline. Various techniques of segmentation have been published [16][17][18]. The 3D centerline from the micro-CBCT slices for this paper is calculated using the semi-automatic region-growing segmentation technique [19].…”

Interactive segmentation of abdominal aortic aneurysms in CTA images

“…However, many medical images are acquired with fixed scan protocols which satisfy these conditions. In case the object axis is known, for instance through a -more easily automated -vessel lumen segmentation (Bulpitt and Berry, 1998;de Bruijne et al, 2003;Fiebich et al, 1997;Giachetti et al, 2002;Magee et al, 2001;Pohle and Toennies, 2001;Subasic et al, 2002;Tek et al, 2001;Weese et al, 2001;Wilson et al, 1999;Wink et al, 2000), or by manual definition, the proposed method could also be used for segmentation in reformatted slices orthogonal to that axis.…”

“…Most publications on computerized AAA segmentation have concentrated on lumen segmentation, either in pre-operative (Bulpitt and Berry, 1998;Fiebich et al, 1997;Giachetti et al, 2002;Magee et al, 2001;Pohle and Toennies, 2001;Rubin et al, 1998;Tek et al, 2001;Weese et al, 2001;Wink et al, 2000) or in post-operative CTA scans . The more difficult problem of segmenting the aneurysm sac has less frequently been addressed.…”

Semi-automated measurement of anatomical structures using statistical and morphological priors

“…Other methods that are able to segment aortic vessel boundaries but not thrombus, and which have significant difficulty with bifurcation points and tortuous vessels include those presented by Wink et al, 12 Verdonck et al, 13 Fiebich et al, 14 and Bulpitt and Berry. 15 …”