For the purpose of obtaining a geometrical model of the wire frame of stent grafts, we propose three tracking methods to segment the stent's wire, and compare them in an experiment. A 2D test image was created by obtaining a projection of a 3D volume containing a stent. The image was modified to connect the parts of the stent's frame and thus create a single path. Ten versions of this image were obtained by adding different noise realizations. Each algorithm was started at the start of each of the ten images, after which the traveled paths were compared to the known correct path to determine the performance. Additionally, the algorithms were applied to 3D clinical data and visually inspected. The method based on the minimum cost path algorithm scored excellent in the experiment and showed good results on the 3D data. Future research will focus on establishing a geometrical model by determining the corner points and the crossings from the results of this method.\u

Klein, A.

Renema, W.K.

Schultze Kool, L.J.

Slump, C.H.

English

For the purpose of obtaining a geometrical model of the wire frame of stent grafts, we propose three tracking methods to segment the stent's wire, and compare them in an experiment. A 2D test image was created by obtaining a projection of a 3D volume containing a stent. The image was modified to connect the parts of the stent's frame and thus create a single path. Ten versions of this image were obtained by adding different noise realizations. Each algorithm was started at the start of each of the ten images, after which the traveled paths were compared to the known correct path to determine the performance. Additionally, the algorithms were applied to 3D clinical data and visually inspected. The method based on the minimum cost path algorithm scored excellent in the experiment and showed good results on the 3D data. Future research will focus on establishing a geometrical model by determining the corner points and the crossings from the results of this method

Klein, Almar

Renema, W.K

Slump, Cornelis H.

University of Twente Research Information

INITIAL STEPS TOWARDS AUTOMATIC SEGMENTATION OFTHE WIRE FRAME OF STENT GRAFTS IN CT DATAA. Klein1, W.K. Renema2, L.J. Schultze Kool2, C.H. Slump11 Twente University, Signals and Systems group, The Netherlands2 Radboud University Nijmegen Medical Centre, The NetherlandsAbstractFor the purpose of obtaining a geometrical model ofthe wire frame of stent grafts, we propose three track-ing methods to segment the stent’s wire, and comparethem in an experiment. A 2D test image was createdby obtaining a projection of a 3D volume containing astent. The image was modified to connect the parts ofthe stent’s frame and thus create a single path. Tenversions of this image were obtained by adding differ-ent noise realizations. Each algorithm was started atthe start of each of the ten images, after which the trav-eled paths were compared to the known correct pathto determine the performance. Additionally, the algo-rithms were applied to 3D clinical data and visuallyinspected. The method based on the minimum costpath algorithm scored excellent in the experiment andshowed good results on the 3D data. Future researchwill focus on establishing a geometrical model by de-termining the corner points and the crossings from theresults of this method.1 IntroductionEndovascular aortic replacement (EVAR) is an estab-lished technique, which uses stent grafts to treat aor-tic aneurysms in patients at risk of aneurysm rupture[2]. The long-term durability of these stent grafts isaffected by the stresses and hemodynamic forces ap-plied to them, and may be reflected by the movementsof the stent graft itself during the cardiac cycle. Apply-ing ECG-gated CTA [7, 11, 9] provides three dimen-sional datasets at different phases of the cardiac cycle,and thus provides insight in the stent’s motion. Thesedata can be used to calculate the forces caused by thehemodynamics [8]. However, this requires obtainingthe geometric model of the stent’s frame in the form ofconnected landmarks placed at corners and crossings.In literature, several studies have been published onthe segmentation of blood vessels. However, two-stepapproaches [13], which first segment the vessel us-ing a vessel measure [6] followed by centerline extrac-tion, are not suitable because of the gaps in the wire(caused by artifacts) and its sharp corners comparedto blood vessels. Methods that fit spheres/ellipsoids tothe vessel (e.g. [1]), and methods that segment thecontour in slices perpendicular to the vessel center-line (e.g. [10]), will not work due to the small diam-eter of the wire (1 to 3 voxels) and its sharp corners.Region growing methods [3] have problems with leaksand gaps and need a second stage to find the geom-etry from the segmented voxels. Methods that use theminimum cost path method [5] are robust against gapsand artifacts (e.g. [4, 12]). However, these methodcannot be applied in their default form since it requiresthe selection of a start point (and often an end point).We propose three tracking algorithms that finds the op-timal path along the stents’ wire, and compare them inan experiment. The proposed methods are applicableto both 2D and 3D (anisotropic) data.2 MethodsThe basic assumption of the proposed methods is thatonce a position on the wire of the stent is known, a nextposition along the wire can be calculated with greatconfidence. Given a 3 dimensional image V (x) withx = (x,y,z), the algorithm places multiple objects in-side the volume, which we refer to as walkers. Thesewalkers are initially positioned in a regular grid with acertain spacing in mm. Next, each walker moves tothe voxel in its local environment that has the high-est intensity, after which each walker will proceed au-tonomously through the data, while tracking the wireframe of the stent.2.1 Method A: 1D growingIn each iteration, this method compares its second or-der neighbors (i.e. its 8 or 26 neighbors for 2D and 3D,respectively) and selects the neighbor with the highestintensity. If the intensity of the selected pixel/voxel isabove a certain threshold, it will be the current positionfor the next iteration, otherwise the walker is discarded.IEEE-EMBS Benelux Chapter Symposium November 9-10, 20092.2 Method B: directional center of massThe second proposed method uses a modified ver-sion of the center-of-mass (COM) method, in whichthe kernels are Gaussian smoothed and multiplied bya direction-dependent weighting kernel. The Gaussiansmoothing is applied to suppress the influence of vox-els further away from the kernel center, and the weight-ing kernel is applied to maintain motion in the directionof the previous step (figure 1). Since the smoothedCOM kernel can be represented by Gaussian deriva-tive kernels, this operator can be considered a gradientoperator biased for a certain direction.We describe the elements of the direction dependentweighting kernel as wk, with k = (kx,ky,kz) the kernelcoordinate relative to the center of the kernel. Giventhe direction of the previous step d, the angle for eachkernel element is αk = arccos(k/|k| ·d/|d|). The weightsof the kernel are calculated with:wk(α) = cos(αk)+0.5. (1)Replacing αk gives:wk(x,d) =x|x|· d|d|+0.5. (2)Each iteration, a patch of data p is sampled, centeredaround the current position. For each dimension, thispatch is multiplied with the corresponding Gaussianderivative kernel and with the weighting kernel to yieldthe new (unnormalized) direction:dnew =(∑∀kpkgk,xwk, ∑∀kpkgk,ywk, ∑∀kpkgk,zwk), (3)where gk,x, gk,y and gk,z represent the Gaussian deriva-tive kernels for the x, y and z direction, respectively.Next, dnew is combined with the old value of d such thatthe angle between the old and the new direction is lim-ited by a predefined value. This angle threshold givescontrol over the walkers’ affinity to proceed in the samedirection, and thus prevents the walker from “making au-turn”.The new position is determined by selecting the voxelswhich are in the found direction, and choosing the onewith the highest intensity.2.3 Method C: minimum cost pathThe third proposed algorithm is based on the minimumcost path algorithm [5, 12], which is applied in a step-by-step tracking approach. Given the speed (repre-senting the inverse cost function) in each voxel sx, theFigure 1: Illustration of the two 2D smoothed COM ker-nels and the direction-dependent weighting kernel.Algorithm 1 The minimum cost path algorithm01 init the time map tx = ∞ ∀x02 init the frozen map fx = 0 ∀x03 init tx = 0 for the start nodes04 insert the start nodes in list L05 while not isempty(L):06 take node m from L with min. tm07 set fm = 108 for n in neighborsOf(m):09 if fn == 0:11 d = eucledianDist(n,m)12 t = tm +d/sn13 if t < tn:14 tn = t15 dn = dm +d16 if n not in L:17 insert n in Lalgorithm iteratively calculates the time tx it takes totravel from the start node to any other node in the im-age (see algorithm 1). The fastest (minimum cost) pathcan be found to any point x by backtracking in the re-sulting time map.We modified the algorithm such that it stops as soon asa distance dx larger than dmin is encountered (figure 2).Since the algorithm selects the node with the minimumcumulative cost (tx) at each iteration, the first node xfor which dx > dmin is the node that is “traveled easiestto”, and therefore has the highest probability of beingpositioned on the same wire as the previous node.To be robust against artifacts, only the first half of thepath from the start node to x is used. This path isappended to the total walked path and its last node willFigure 2: Illustration of the proposed minimum costpath approach. The front (the voxels in L) moves out-ward until the cumulative distance of the path is largerthan dmin.IEEE-EMBS Benelux Chapter Symposium November 9-10, 2009(a) (b)Figure 3: Illustration of the 2D test image (a) and theused mask to calculate performance(b).be the start node for the next iteration. At the start ofthe next iteration the last 20 nodes in the total walkedpath are set frozen to prevent backtracking.The speed sx is calculated with:sx = 2V (x)/δ , (4)where δ is a scalar number. Using this speed map hasthe effect that the path through a single voxel with a CT-value of a, is equally fast as another path through twovoxels with CT-values of a +δ . In other words, the op-timal path has a strong affinity for high intensity voxels,rather than taking the shortest route. This prevents thealgorithm to cut the sharp corners present in our data.In our method we used a value of δ = 100.3 ExperimentsA 2D test image (figure 3a) was created by obtain-ing a projection of a 3D volume containing a stent.The image was modified to connect the three parts ofthe stents’ frame and thus create a single path. Tenversions of this image were obtained by adding differ-ent noise realizations (Gaussian noise with a standarddeviation of 75 CT-values). A walker was placed towalk down the path of the ten images, after which thetraveled paths were compared to the known correctpath (figure 1b) to determine the performance of eachmethod. Beforehand, the same setup was used (butwith different noise realizations as in the experiment)to choice the optimal parameters for each method.Additionally, the algorithms were applied to 3D clinicaldata using multiple walkers, and the result was visuallyinspected.4 Results and DiscussionFrom the average correspondence of the walked pathswith the correct path (table 1) it can be seen that themethod A performed poorly, while method C performedMethod: A B CPerformance: 0.32 0.88 1.00Table 1: The quantitative results of the experiments.(a) (b) (c)Figure 4: Illustration of the walked paths of the threemethods for the 10 noise realizations.excellent. The traveled paths for each method (fig-ure 4) confirm these findings. Visual inspection of themethod applied to 3D data (figure 5) shows the sametrend.During the process of the experiments, it was foundthat the performance of method B is sensitive to thechosen parameters (such as the scale of the kernelsand the angle limit). In contrast, method C was veryrobust for the choice of its parameter values (dmin andδ ).We note that method C can be seen as a generaliza-tion of method A; instead of selecting the next positionfrom the direct neighbors, the most probable voxel at acertain distance from the current position is selected.The concept of incorporating a larger neighborhoodin determining the most probable next position is alsoused in method B. However, method B is sensitive fornearby structures (e.g. other wires) and has problemswith sharp corners.In the current method, the walkers are unaware of eachother. By connecting the walked paths when two walk-ers meet, the walkers should be able to work togetherto perform the segmentation task.5 Conclusions and Future WorkWe have described and compared three methods totrack the wire frame of the stent graft in 3D CT data and2D projections thereof. The best performing method isbased on the minimum cost path method and is capa-ble of obtaining near-perfect segmentation of the stent.The method is robust to unforeseen situations and vari-ation in its parameters.Future research will focus on establishing a geomet-rical model by determining the corner points and theIEEE-EMBS Benelux Chapter Symposium November 9-10, 2009(a) (b) (c)Figure 5: Illustration of the found paths of multiplewalkers in a 3D volume.crossings from the results of the proposed method.6 References[1] Thomas Beck, Christina Biermann, Dominik Fritz,Rudiger Dillmann, Josien P. W. Pluim, and Benoit M.Dawant. Robust model-based centerline extraction ofvessels in CTA data. In Medical Imaging 2009: Im-age Processing, volume 7259, pages 72593O–9, LakeBuena Vista, FL, USA, February 2009. SPIE.[2] Jan D. Blankensteijn, Sjors E.C.A. de Jong, MoniquePrinssen, Arie C. van der Ham, Jaap Buth, Steven M.M.van Sterkenburg, Hence J.M. Verhagen, Erik Buskens,Diederick E. Grobbee, and the Dutch Random-ized Endovascular Aneurysm Management (DREAM)Trial Group. Two-Year outcomes after conventional orendovascular repair of abdominal aortic aneurysms. NEngl J Med, 352(23):2398–2405, June 2005.[3] Susanne Bock, Caroline Kuhhnel, Tobias Boskamp,and Heinz-Otto Peitgen. Robust vessel segmentation.volume 6915, page 114, April 2008.[4] Thomas Deschamps and Laurent D Cohen. Fast ex-traction of minimal paths in 3D images and applica-tions to virtual endoscopy. MEDICAL IMAGE ANALY-SIS, 5:281—299, 2000.[5] E.W. Dijkstra. A note on two problems in connexion withgraphs. Numerische Mathematik, 1:269—271, 1959.[6] Alejandro F Frangi, Wiro J Niessen, Koen L Vincken,and Max A Viergever. Multiscale vessel enhance-ment filtering. Lecture Notes in Computer Science,1496:130—137, 1998.[7] T. O. Fuchs, M. Kachelriess, and W. A. Kalender. Sys-tem performance of multislice spiral computed tomog-raphy. IEEE Engineering in Medicine and Biology,19:63—70, 2000.[8] Almar Klein, Luuk J. Oostveen, Marcel J. W. Greuter,Yvonne Hoogeveen, Leo J. Schultze Kool, Cornelis H.Slump, and W. Klaas Jan Renema. Detectability of mo-tions in AAA with ECG-gated CTA: a quantitative study.Medical Physics, 36(10):4616–4624, October 2009.[9] Almar Klein, W. KlaasJan Renema, Luuk J. Oost-veen, Leo J. Schultze Kool, and Cornelis H. Slump.A segmentation method for stentgrafts in the abdomi-nal aorta from ECG-gated CTA data. In Proceedingsof SPIE, pages 69160R–69160R–10, San Diego, CA,USA, 2008.[10] Jack Lee, Patricia Beighley, Erik Ritman, and Nico-las Smith. Automatic segmentation of 3D micro-CTcoronary vascular images. Medical Image Analysis,11:630—647, 2007.[11] Bart E. Muhs, K. L. Vincken, J. van Prehn, M. K. Stone,L. W. Bartels, M. Prokop, F. L. Moll, and H. J. Verhagen.Dynamic Cine-CT angiography for the evaluation of thethoracic aorta; insight in dynamic changes with implica-tions for thoracic endograft treatment. European journalof vascular & endovascular surgery, 32(5):532—536,2006.[12] Onno Wink, Alejandro F Frangi, Bert Verdonck, Max AViergever, and Wiro J Niessen. 3D MRA coronary axisdetermination using a minimum cost path approach.Magnetic Resonance in Medicine: Official Journal ofthe Society of Magnetic Resonance in Medicine / So-ciety of Magnetic Resonance in Medicine, 47(6):1169–1175, June 2002. PMID: 12111963.[13] Stefan Worz, William J. Godinez, Karl Rohr, JosienP. W. Pluim, and Benoit M. Dawant. Segmentation of3D tubular structures based on 3D intensity models andparticle filter tracking. In Medical Imaging 2009: Im-age Processing, volume 7259, pages 72591P–8, LakeBuena Vista, FL, USA, February 2009. SPIE.IEEE-EMBS Benelux Chapter Symposium November 9-10, 2009

Initial steps towards automatic segmentation of the wire frame of stent grafts in CT data

NARCIS 

3D MRA coronary axis determination using a minimum cost path approach. Magnetic Resonance in Medicine:

A note on two problems in connexion with graphs.

A segmentation method for stentgrafts in the abdominal aorta from ECG-gated CTA data.

Automatic segmentation of 3D micro-CT coronary vascular images.

Caroline Kuhhnel, Tobias Boskamp, and Heinz-Otto Peitgen. Robust vessel segmentation.

Dynamic Cine-CT angiography for the evaluation of the thoracic aorta; insight in dynamic changes with implications for thoracic endograft treatment.

Erik Buskens, Diederick E. Grobbee, and the Dutch Randomized Endovascular Aneurysm Management (DREAM) Trial Group. Two-Year outcomes after conventional or endovascular repair of abdominal aortic aneurysms.

Fast extraction of minimal paths in 3D images and applications to virtual endoscopy.

Multiscale vessel enhancement ﬁltering.

Renema. Detectability of motions in AAA with ECG-gated CTA: a quantitative study.

Robust model-based centerline extraction of vessels in CTA data.

Segmentation of 3D tubular structures based on 3D intensity models and particle ﬁlter tracking.

System performance of multislice spiral computed tomography.

http://purl.utwente.nl/publications/68418

Initial steps towards automatic segmentation of the wire frame of stent grafts in CT data

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