Automatic Segmentation of the Aortic Dissection Membrane from 3D CTA Images

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1 Automatic Segmentation of the Aortic Dissection Membrane from 3D CTA Images Tamás Kovács 1, Philippe Cattin 1, Hatem Alkadhi 2, Simon Wildermuth 3, and Gábor Székely 1 1 Computer Vision Group, ETH Zurich, 8092 Zurich, Switzerland 2 Institute of Diagnostic Radiology, University Hospital, 8092 Zürich 3 Institut fuer Radiologie, Kantonsspital St. Gallen, 9000 St. Gallen, Switzerland {kovacs,cattin,szekely}@vision.ee.ethz.ch, hatem.alkadhi@usz.ch, simon.wildermuth@kssg.ch Abstract. Acute aortic dissection is a life-threatening condition and must be diagnosed and treated promptly. For treatment planning the reliable identification of the true and false lumen is crucial. However, a fully automatic Computer Aided Diagnosis system capable to display the different lumens in an easily comprehensible and timely manner is still not available. In this paper we present a method that segments the entire aorta and then identifies the two lumens separated by the dissection membrane. The algorithm misdetected part of the membrane in only one of the 15 cases tested, where the aorta has not been significantly altered by the presence of aneurisms. 1 Introduction Acute aortic dissection is a life-threatening condition and must be diagnosed and treated promptly. Exact knowledge of the lumen and dissection membrane topology is vital for treatment planning. This is unfortunately a difficult task, even for trained professionals. A fully automatic Computer Aided Diagnosis (CAD) system capable to display the different lumens in an easily comprehensible way is still not available. It is thus the aim of this research to provide the radiologist generic tools to support diagnosis and treatment planning. In order to implement such a CAD system, firstly an accurate segmentation of the entire aorta including the aortic arch as well as the ascending- and descending sections must be performed. Secondly the dissection membrane must be detected in order to identify the true and false lumen. Different solutions for the aorta segmentation can be found in literature. These include vessel axis extraction and border estimation, neural networks, region growing or watershed- and level set-based approaches. An extensive survey of these approaches is given in [1]. All of these methods have serious limitations like segmenting just a well defined part of the aorta or requiring manual initialization, sometimes even user intervention. Moreover, these methods are not robust against the disturbing artifacts in the image data like the inhomogeneous

2 density of the contrast agent caused by flow variations in the lumens, reconstruction errors due to high contrast agent density [2] and last but not least they are not able to deal with the dissection membrane within the aortic lumen. Behrens described in [3] a Hough transform based algorithm combined with a Kalman filter. The drawback of this approach was, however, the need for three user-selected parameters namely, the starting point of the aorta, the aorta radius, and an approximate axis direction. In [4] we presented a fully automatic segmentation method of the aorta using a Hough transformation based initialization of a deformable surface model. In comparison to Behrens approach, our method incorporates more a priori anatomical knowledge about the shape of the aortic arch and therefore needs no manual initialization. In this paper we focus on the detection of the aortic dissection membrane, which is the final step towards a fully automatic CAD system. As the membrane is a flat elongated structure, sheet detection methods can be used for its localization. Only very few sheet detection methods have been reported in the literature and almost all of them are based on the eigenvalue analysis of the Hessian matrix [5 7]. Descoteaux proposed in [8] a method that combines the eigenvalues of the Hessian matrix in one single measure. A completely different multiscale line detector based on steerable filters was proposed by Koller in [9]. The approach can be extended to 3D for sheet detection in a straightforward manner. While the resulting non-linear filter is strictly selective to sheet-like structures and does not respond to edges, our experiments showed that Koller s method is computationally less efficient than Hessian matrix based methods. In the following section we describe the newly developed method for the automatic identification of the true and false aortic lumens. Section 3 presents the results of our approach on 19 clinical cases and their validation against manual segmentation. The paper is then concluded by the discussion of the achievements and ongoing and future work. 2 Method For completeness we first summarize the aorta segmentation method we use [4]. The method is based on the observation that on the one hand the aorta lumen has an approximately circular cross section and on the other hand the aortic arch forms a 180 -sector of a torus. In the first step an initial slice superior to the heart has to be found, on which two seed points for the ascending and the descending segment can be marked. In order to obtain this initial slice the region of the heart is detected by calculating the average intensity of all axial slices. Starting from the slice with the highest average intensity, 20 axial slices are taken with an inter slice distance of 1 cm. On each of these slices the two circles corresponding to the strongest Hough-peaks are selected, as these are mostly generated by the ascending and descending aorta. These Hough-peaks are then projected to an axial plane. The set of the points on this plane is then spatially clustered with the K-mean algorithm. The centers of the two largest clusters are taken as the centers of the ascending and

3 descending aorta and the slice with these two clusters closest to the heart is taken as the initial slice as illustrated by Fig. 1(a)/1. The relative position of these points is then used as a first rough estimate for the position of the aortic arch (Fig. 1(a)/2). This approximation is then refined by using an additional Hough transformation on a reformatted slice perpendicular to the line connecting the two circle centers (Fig. 1(a)/3,4). The exact location of the aortic arch is then found by Hough transformations on reformatted slices perpendicular to this first centerline estimate as shown in. An initial mesh of the lumen is generated using the centers (Fig. 1(b)/5) and the radii of the detected circles. The lumen is finally identified by elastically deforming the initial mesh to match the edges corresponding to the vessel wall. (a) (b) Fig. 1. (a) The detection of the three initial circles to estimate the parameters of the aortic arch (b) the refinement of the aortic arch and the descending aorta with additional circles. The steps described in the text are highlighted with numbers Once the aorta lumen is segmented, it is used as a region of interest (ROI) for the detection of the dissection membrane. To further ease the membrane detection, the aorta is straightened along its centerline. The resulting stack consist of reformatted slices, locally perpendicular to the vessel. In order to localize the membrane we use Descoteaux s sheetness measure [8], which is making simultaneous use of the three Hessian eigenvalues in a comprehensive way. Based on the ellipsoid model described in [7], this detector assigns a score S(p, σ) [0, 1] to each voxel p at scale σ which can be interpreted as the probability to be part of a sheet-like structure, S(p) = max σ Ω S(σ) = 0 if λ 3 < 0 «[ «] [ «] R sheet 2 R 2 2α e 2 blob R 2β 1 e 2 noise 2 2c 1 e 2 else (1)

4 where R sheet = λ 2 λ 3 (2) R blob = (2 λ 3 λ 2 λ 1 ) (3) λ 3 R noise = 3 λ 2 j (4) j=1 with λ 1 λ 2 λ 3 the eigenvalues of the Hessian matrix computed at point p with scale σ. Ω is a finite set of scales covering the range between the smallest and the largest expected membrane thickness. For our experiments we have chosen three different scales Ω={1 mm, 1.5 mm, 2 mm}. The parameters α, β and c control the sensitivity of the sheet detector and were set to α = 0.5, β = 0.5, for c half the value of the maximum Hessian norm has been selected. To avoid the big computational overhead required for the calculation of c Felkel et al. proposed in [10] to use 0.25σ 2 I max instead of 2c 2 for the normalization of the third exponent. According to our experience this results in faster but less accurate membrane detection, therefore we decided not to use this alternative for the calculation of the sheetness measure. As the approach described above is quite sensitive to image noise, the ROI is pre-processed with a curvature flow filter [11] to increase the robustness of the membrane detection. Several factors influence the detection of the membrane, leading often to the generation of false positive or false negative candidates. On the one hand, image noise and inhomogeneous contrast agent distribution can create fake membrane structures. As these are only visible in one or a few slices, they can be efficiently filtered out using 3D connected component labeling. In fact, in all our experiments the largest connected component was always the dissection membrane itself. On the other hand, reformatting blur, partial volume effects, thin membranes, and previous surgical interventions can lead to gaps in the detected dissection membrane. At least one tear in the dissection membrane, where the blood enters the inner lining of the aorta (intima) and thus creates the false lumen, has to be present near to the proximal side of the dissection. Often more gaps and tears appear due to the aforementioned effects. A robust method has therefore to be found to replace the missing parts of the membrane. Since the above described sheetness measure is not able to detect the membrane close to other structures (see Fig. 2(a)), extrapolation is necessary to connect the membrane with the aortic wall. For this, the centerline of the membrane is generated on every slice i using binary thinning. If the centerline is not connected on the slice, it is skipped and the connection points with the wall (wallpoints) are interpolated at a later stage from adjacent slices. Otherwise the two endpoints (P i, P i ) are linearly extrapolated towards the aortic wall. To incorporate the orientation of the membrane into the extrapolation, the directions of continuation v i,0 and v i,0 are first set according to the estimated tangent of the two endpoints, see Fig. 2(a). By pivoting v i,0 around P i with an angle of

5 (a) (b) Fig. 2. (a) Extrapolation of the membrane to the aortic wall as a straight continuation. (b) The determination of the real connection point used to initialize the sheet mesh ω = k 5 k Z ω 30 several wallpoint candidates {W i,30, W i,25... W i,ω... W i, 30 } are generated. The wallpoint candidate with the lowest average intensity along the line segment connecting to P i is then finally chosen. v i,0 is processed in an identical fashion. Once the wallpoints for all slices are calculated, the initial mesh of the membrane can be generated using the wallpoints as constraining nodes. On each slice the connecting line between the two wallpoints is split in equidistant points. The mesh is generated by triangulating between these points of adjacent slices. It is then iteratively fitted to the dissection membrane using the mass-spring approach presented by Brown in [12]. The segmentation process follows the procedure described by [13]. According to Zsemlye, mass points are associated with each node and springs with each edge of the mesh. The nodes are under the influence of internal and external forces. The internal forces keep the surface smooth, whereas the external forces deform the mesh in order to match the underlying CT dataset. In order to obtain accurate membrane segmentation, we used the Gradient Vector Flow (GVF) proposed by Xu et al. [14] as external force. 3 Results The proposed method for membrane detection was tested on 19 patients with an abdominal aortic dissection. Of these 19 patients 4 had in addition to the dissection an aortic aneurism. The CT datasets typically consisted of slices of pixels using 16-bit quantization, with a pixel size of mm, and an effective slice thickness of 2 mm. The method was successful on 15 cases, but failed for the 4 patients with the aneurism. The reason was that the aorta segmentation method can not yet handle the varying lumen diameter through the aneurisms. The membrane detection could therefore not be tested, as the aorta lumen has to be known

6 beforehand to restrict the ROI. Additionally, the proposed approach could not properly segment the dissection membrane in one case, where over a range of 12 slices three lumens are visible, see Fig. 4(a). This type of split or highly folded membranes can of course not be modeled with our sheet mesh. In spite of the sometimes bad signal-to-noise ratio, the detection process was successful in all other cases, as demonstrated by some examples in Fig. 3. (a) (b) (c) Fig. 3. (a) Accurate segmentation of the lumens in the aortic arch displayed on an axial slice (b) membrane detection in the ascending as well as in the descending aorta (c) 3D rendering of the true and false lumen To obtain a quantitative assessment, the aorta and the membrane in every dataset were manually segmented by an expert observer. As the dissection membrane is generally a several voxels wide thick structure, manual segmentation is highly subjective and observer dependent. To avoid this ambiguity the expert was instructed to place the borderline between the lumens in the middle of the membrane wherever possible. The mesh of the segmented true and false lumens were then taken as the ground truth and compared to the results of the 15 successful automatic segmentations. To quantify the performance, we calculated the average distance of the two meshes. Our method resulted in an overall average error of 1.71 ± 1.89 mm. The best segmentation had an average distance of 1.13 ± 0.63 mm and the worst one of 1.92 ± 2.00 mm. Several problems limit the performance of the membrane detection. Firstly, the exact position of the membrane close to the aortic wall is ambiguous, see Fig. 4(b). The membranes generally widen near the wall and it is therefore not possible to determine its exact location. Fig. 4(c) depicts the manual delineation of the membrane on the same slice. It can be seen that the observer had exact knowledge about the true lumen and this was incorporated into the segmentation process, as the membrane usually curves towards the true lumen. Secondly, in areas where the membrane has no visible attachment to the wall as in Fig. 4(d) the extrapolation method often leads to unintuitive results. A smoothing along

7 the wallpoints should reduce the deviations from the wallpoints a human expert would have selected. Thirdly, the membrane can not yet be accurately detected close to calcified plaques as can be seen in Fig. 4(e). In the worst case a membrane is recognized but the plaque being assigned to the wrong lumen as shown in Fig. 4(f). As plaques can only appear in the true lumen such membrane misdetections can lead to wrong identification and consequently lead to false treatment plans. (a) (b) (c) (d) (e) (f) Fig. 4. (a) Special case with three visible lumens and the result of membrane segmentation in black (b) Comparison of the automatic detection and (c) the manual segmentation of the membrane. (d) Unintuitive membrane extrapolation. (e,f) Erroneous membrane detections due to the presence of calcified plaques. Manual segmentation of the aorta and the membrane required min per dataset. In contrast, the processing time was less than 6 min for the fully automatic aorta segmentation and 7 9 min for the membrane detection procedure using a standard 2.4 GHz PC. 4 Conclusions In this paper we presented a fully automatic method for the detection of the membrane in abdominal aortic dissections. The detection method is based on eigenvalue analysis of the Hessian matrix and a related sheetness measure. To close the inevitable gaps in the segmented dissection membrane, an elastically

8 deformable 3D sheet model is used. The method has been evaluated on angiographic CT images and the results indicate that the proposed method has a high reproducibility, clinically acceptable accuracy and is faster than manual segmentation. Our future work will concentrate on the identification of the true and false lumen based on indicators such as (1) calcifications (should be only in the true lumen), (2) the angle between the aortic wall and the membrane and (3) the different densities of contrast agent caused by flow variations in the two lumens. A larger evaluation study with manually segmented lumens from multiple experts is planned to asses the reliability of the proposed aortic dissection analysis tool. Acknowledgments The authors thank Dr. Tobias Boskamp and Olaf Konrad-Verse from MeVis (Center for Medical Diagnostic Systems and Visualization) Bremen, Germany for providing and supporting the MeVisLab working environment. This work has been supported by the CO-ME/NCCR research network of the Swiss National Science Foundation ( References 1. B.H.Sollie: Automatic segmentation and registration of CT and US images of abdominal aortic aneurysm using ITK (2002) Masterthesis: Norwegian University of Science and Technology, Norway. 2. R.Baissalov, G.A.Sandison, B.J.Donnelly, et al.: Suppression of high-density artefacts in x-ray CT images using temporal digital subtraction with application to cryotherapy. Phys Med Biol 45 (2000) T.Behrens, K.Rohr, H.S.Stiehl: Robust segmentation of tubular structures in 3-D medical images by parametric object detection and traking. IEEE Trans Syst Man Cybern B 33 (2003) Y.Sato, C.Westin, A.Bhalerao, et al.: Tissue classification based on 3d local intensity structures for volume rendering. IEEE Trans Vis Comput Graph 6 (2000) K.Krissian, G.Malandain, N.Ayache, et al.: Model-based detection of tubular structures in 3d images. Comput Vis Image Underst 80 (2000) A.F.Frangi, W.J.Niessen, K.L.Vincken, et al.: Multiscale vessel enhancement filtering. Lecture Notes in Comput Science 1496 (1998) M.Descoteaux, M.Audette, K.Chinzei, el al.: Bone enhancement filtering: Application to sinus bone segmentation and simulation of pituitary surgery. In: MICCAI. (2005) T.Koller, G.Gerig, G.Szekely, et al.: Multiscale detection of curvilinear structures in 2d and 3d image data. In: ICCV. (1995) P.Felkel, R.Wegenkittl, A.Kanitsar: Vessel tracking in peripheral cta datasets an overview. In: SCCG 01. (2001) Sethian, J.: Level Set Methods and Fast Marching Methods. Cambridge Press (1999) Chapter 16.

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