A Hybrid Method for Coronary Artery Stenoses Detection and Quantification in CTA Images

Transcription

1 A Hybrid Method for Coronary Artery Stenoses Detection and Quantification in CTA Images İlkay Öksüz 1, Devrim Ünay 2, Kamuran Kadıpaşaoğlu 2 1 Electrical and Electronics Engineering, Bahçeşehir University, Istanbul, Turkey 2 Biomedical Engineering, Bahçeşehir University, Istanbul, Turkey {ilkay.oksuz, devrim.unay, kamuran.kadipasaoglu}@bahcesehir.edu.tr Abstract. This paper describes a hybrid algorithm to detect and quantify the coronary artery stenoses in 3D CT Angiography images. The approach combines the Hessian matrix based vesselness filter with three dimensional region growing algorithm for segmentation of the coronary arteries. Centerlines of the segmented arteries are extracted using an in-house fast marching based method. Detection and quantification of the stenoses are then accomplished by estimating the vessel diameter at each centerline location via plane fitting, and applying linear regression analysis on the estimated diameter profile. For the detection stage, a sensitivity of 21% and a PPV of 33% are achieved as compared to QCA, while a sensitivity of 17% and a PPV of 25% are achieved as compared to CTA. However, the stenoses are quantified with an averaged absolute difference of only 44.9% as compared to QCA. The approach allowed evaluation of average data within ten minutes. In conclusion, the algorithm performed relatively well for detection purposes (threshold at 50% diameter reduction); but it remains to be improved for better categorization of severity of stenosis as mild, moderate, and severe. Keywords: Medical Image Segmentation, Coronary Arteries, 3D CTA, Stenoses Detection, Stenoses Quantification, Hessian Matrix, Frangi Vesselness, Region Growing, K-means Clustering, Centreline Extraction, Plane Fitting 1 Introduction Segmentation of vascular structures, an indispensible part of biomedical imaging applications, is an important step towards diagnosis and planning of the surgical treatment of vascular disorders. Modern 3D angiography produces increasingly accurate image acquisition modalities and detailed images that have to be analyzed and interpreted by medical experts. Accordingly, the need for automated or semi-

2 automated segmentation methods for image processing has become more acute than ever to reduce the burden on the experts. In segmenting the vascular structures from angiographic images minimal path based approaches, which define a vessel as a path between two points on a regular lattice, have been particularly popular [1]. Raman and Then presented a hybrid, semiautomated method, that sequentially applies fast marching algorithm and level sets; and detects coronary arterial contours by the minimum-cost path approach [2]. Main disadvantage of this system is the interactive control needed during the process. Another work making use of the same approach is introduced by Benmansour and Cohen [3]. Their anisotropic filter-based algorithm requires user interaction, and therefore is slow; nevertheless, it is robust to bifurcations and provides the radius information of the coronary arteries. Chen et al. s solution finds the lines of the vessels by making use of geometric moments, and calculates the vessel radius with the help of snakes [4]. While the literature on vascular segmentation is vast [1], stenosis detection has been studied less. A recent work on the topic is presented by Yan et al, who quantify coronary arterial Stenoses using a fuzzy distance transform, which reportedly achieves a high accuracy (kappa coefficient of 87.9%), even in small datasets (n=13 patients) [5]. Stenosis and calcification of coronary arteries are life threatening issues for humans. Segmentation of the coronary arteries is a crucial problem due to the small size and convoluted anatomy of, and variations in the abnormalities in these vessels. Recently, computer-aided diagnosis systems have become key components of medical treatment processes, of which coronary artery segmentation algorithms are the critical components. In this paper, we present a hybrid method based on combined usage of Hessian matrix based vesselness filter and three dimensional region growing for robust detection and quantification of multiple coronary stenoses with different types and significance from 3D CT angiography datasets. The method is developed within the Stenoses Detection and Quantification Grand Challenge of the Rotterdam Coronary Algorithm Evaluation Framework. In our method, diameter calculation from the segmented arteries is realized via plane fitting at multiple locations along the arteries. Then, we quantify the stenosis along the arteries and express it as a percentage of diameter change at any given centerline location. 2 Data The challenge data consist of 18 training and 24 testing CT angiography data with varying total slice numbers and slice thicknesses acquired at three different medical sites. Furthermore each training dataset is accompanied by the world position, anatomical location, and stenosis information (number, type and severity grade: only for the training set) of multiple centerline points from the coronary arteries [6].

3 3 Method 3.1 Preprocessing The acquired images further include the pulmonary vessels observed as high intensity regions, which need to be excluded from the analysis, to which end, a threshold of -400 HU (Hounsfield Unit) is used. The extracted region is used as a mask with the help of morphological dilation. The voxels with intensities higher than 500 HU (empirically set based on the training data) is also removed due to the fact that they belong to a calcified region, which are not part of a vessel lumen and thus could lead to false segmentations (Fig. 1). Fig. 1. Example of preprocessing. Voxels with intensity above 500HU are removed in the original image (left) to eliminate calcified regions (arrow). 3.2 Frangi Vesselness In this work, the Hessian matrix based method proposed by Frangi [7] for extracting tubular structures from an image is used to determine the vesselness of any voxel in the data. The method uses the cylindrical structure of the vessels and segments them employing a line enhancement filter. The Hessian matrix consists of the second order gradients of the input image. The orientation of the eigenvalue of the matrix is the basis for the vesselness filter [ ] (1) where I refers to the image and is the gradient operator, composed of the respective gradients of the three dimensional image. The calculation of Hessian matrix H is repeated at each voxel location with different scales. Using these values, a vesselness value can be calculated [7]. (2)

4 (3) (4) { ( ) (5) where are the Hessian matrix eigenvalues, from which values of RA, RB and S are calculated; V is the normalized vesselness value for each voxel on the data; and α, ß and represent the weights. The scale is empirically set to 30 following the experiments on the training dataset. Figure 2 shows the eigenvectors corresponding to the eigenvalues. Fig.2. Eigenvectors in Frangi vesselness filter 3.3 3D Region Growing In order to segment the points belonging to the coronary arteries, a 3D region growing operation, with similarity criterion being the absolute intensity difference between a candidate voxel and the running average intensity of the region, is used on the vesselness maps of the data. If a candidate voxel s intensity falls within 5% of the average intensity of the region, then it is added to the region. To initialize the region growing algorithm, multiple seed points from both right and left coronary arteries are employed. An exemplary segmentation result of a challenge dataset is shown in Fig. 3. Centerlines of the segmented arteries are then extracted using an in-house fast marching based method. Finally, the resulting point cloud is input into the subsequent stenosis detection algorithm.

5 Fig. 3. The extracted coronary arterial tree for the challenge dataset No 6. The 3D meshes of the tree (overlaid on the original image) are displayed from two different views. 3.4 Vessel Diameter Estimation via Plane Fitting Plane fitting is performed for every centerline point, where the corresponding vessel diameter is determined. As a plane can be described with a point and a normal to the plane, we employ the detected centerline point and the average dominant eigenvector of the Hessian matrix computed at a 5 3 neighborhood of that location as the normal vector. For this purpose, we make use of a modified version of the familiar plane equation defined in a three-dimensional space (6)

6 where the array ( ) refers to the normal vector, is an empirically set threshold ( =2 voxels), and the arrays and define the Cartesian coordinates of the centerline location and that of another arbitrary point from the lumen data, respectively. Furthermore, in order to eliminate false detections due to bifurcations, we employ k-means clustering (k=2) and symmetry-around-centerlinepoint check (if between-class variation is larger than twice the within-class variation, then some lumen data points are assumed to correspond to a bifurcation region and the class closest to and surrounding the centerline point is retained). Accordingly, luminal data points that satisfy the above approach are said to belong to the plane-ofinterest and, thus, are used to compute the diameter at the corresponding centerline location. Here the diameter is defined as twice the average Euclidean distance between the lumen points (satisfying Equation 6) and the corresponding centerline location. 3.5 Detection and Quantification of Stenoses Local diameter variation along an artery can be a sign of stenosis. In order to quantify stenosis, the arterial diameter profiles are used. 1D running window (size 5) based median filtering followed by smoothing (m, measured) is applied to the profile data (Fig. 4), and nominal vessel diameter (, predicted) is predicted by linear regression. Subsequently, stenosis at each centerline location is quantified as { (7) In the proposed method, the ratio percentage of the smallest (worst) vessel diameter at a stenosis to the diameter immediately adjacent characterizes the CTA. On the other hand, based on clinical insight and considering the annotations provided with the challenge training data, the QCA grade at each segment is defined to be proportional to the length of the significant stenosis it contains. Here a stenosis is defined as significant if it shows greater than or equal to 50% narrowing in vessel diameter. Fig. 4. Stenosis detection example. Segmented vessel with the detected centerline (Left). Corresponding stenosis scores as percentage.

7 4 Experimental results The algorithm is tested on the Challenge testing data, consisting of 24 CTA images and the results of this method are based on the manually corrected centerlines obtained from Yang et al. [6]. The quantitative results for detection and quantification of stenoses on the testing data are provided in Tables 1 and 2, respectively. For the detection stage, a sensitivity of 21% and a positive predictive value (PPV) of 33% are achieved as compared to QCA, while a sensitivity of 17% and a PPV of 25% are obtained as compared to CTA. However, the stenoses are quantified with an averaged absolute difference (Avg.Abs. diff.) of only 44.9% as compared to QCA. Thus, although the algorithm performed relatively well for detection (threshold at 50% diameter reduction), it remains to be improved for better categorization of the stenosis degree between mild, moderate, severe and occlusion grades. When compared to the expert annotations, our algorithm mostly falls behind the observers performance in both detection and quantification accuracy. Finally, the average execution time of our algorithm for a CTA image on a 2.4 GHz processor is approximately ten minutes. Table 1. Detection results for the testing data QCA QCA CTA CTA Method Sensitivity PPV Sensitivity PPV Avg. rank % rank % rank % rank % rank Proposed Observer Observer Observer Table 2. Quantification results for the testing data QCA QCA CTA Method Avg. Abs. diff. R.M.S. diff. Weigthed Kappa Avg. rank % rank % rank Κ rank Proposed Observer Observer Observer Discussion and conclusion The algorithm presented here may be promising novel semi-automated solution for coronary arterial stenosis detection and quantification. Quantitative evaluation showed that it achieves low sensitivity, but an acceptable positive predictive value. The algorithm should be modified to improve stenosis detection and quantification accuracy, as well as to minimize user interaction and execution time. Also, as adja-

8 cent stenoses in the training data are lumped in a single stenosis and classified into a single category, modification of the present algorithm is warranted towards dividing a segment into its multiple stenoses and treating each as a separate entity. Acknowledgments The authors would like to thank Professor M. Savaş Tepe, M.D., from Department of Radiology, Bayındır Hospital İçerenköy, İstanbul, and Professor Seçkin Pehlivanoğlu, M.D., from Department of Cardiology, Başkent University Hospital, İstanbul, for providing valuable clinical insight on the challenge data. This work is supported by the Turkish Ministry of Science, Industry, and Technology (grant number STZ ). References 1. Quek F., Kirbas C.: Vessel extraction in medical images by wave propagation and traceback. IEEE Trans. Med. Imaging 20(2), (2001). 2. Raman V., Then P.: Novelty towards hybrid segmentation of coronary artery in CT cardiac images. In: Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, pp (2008). 3. Benmansour F.,Cohen L.D.: A new interactive method for coronary arteries segmentation based on tubular anisotropy. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp (2009). 4. Chen K., Zhang Y., Pohl K., Syeda-Mahmood T., Song Z., Wong S.T.C.: Coronary artery segmentation using geometric moments-based tracking and snake-driven refinement. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), pp (2010). 5. Xu Y., Liang G., Hu G., Yang Y., Geng J., Saha P.K.: Quantification of coronary arterial stenoses in CTA using fuzzy distance transform. Comput. Med. Imaging Graph., 36(1), (2012). 6. Yang G., Broersen A., Petr R., Kitslaar P., de Graaf M.A., Bax J.J., Reiber J.H.C., Dijkstra J.: Automatic coronary artery tree labeling in coronary computed tomographic angiography datasets. Comput. Cardiol. 38, (2011). 7. Frangi A.F., Niessen W.J., Vincken K.L., Viergever M.A.:Multiscale vessel enhancement filtering. In: MICCAI 98, W.M. Wells, A. Colchester, S.L. Delp (Eds.), LNCS, vol.1496, pp , Springer-Verlag, Berlin, Germany (1998).