Abstract
The objective of this contribution consists in segmenting dissected aortas in computed tomography angiography (CTA) data in order to obtain morphological specifics of each patient’s vessel. Custom-designed stent-grafts represent the only possibility to enable minimally invasive endovascular techniques concerning Type A dissections, which emerge within the ascending aorta (AA). The localization of cross-sectional aortic boundaries within planes orthogonal to a rough aortic centerline relies on a multicriterial 3D graph-based method. In order to consider the often non-circular shape of the dissected aortic cross-sections, the initial circular contour detected in the localization step undergoes a deformation process in 2D, steered by either local or global statistical distribution metrics. The automatic segmentation provided by our novel approach, which widely applies for the delineation of tubular structures of variable shapes and heterogeneous intensities, is compared with ground truth provided by a vascular surgeon for 11 CTA datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Avila-Montes, O.C., Kukure, U., Kakadiaris, I.A.: Aorta segmentation in non-contrast cardiac CT images using an entropy-based cost function. In: SPIE Medical Imaging, pp. 76233J–76233J-8. International Society for Optics and Photonics (2010)
Barber, C.B., Dobkin, D.P., Huhdanpaa, H.: The quickhull algorithm for convex hulls. ACM Trans. Math. Softw. (TOMS) 22(4), 469–483 (1996)
Ben Ayed, I., Li, S., Ross, I.: Embedding overlap priors in variational left ventricle tracking. IEEE Trans. Med. Imaging 28(12), 1902–1913 (2009)
Biesdorf, A., Rohr, K., Feng, D., von Tengg-Kobligk, H., Rengier, F., Böckler, D., Kauczor, H.U., Wörz, S.: Segmentation and quantification of the aortic arch using joint 3D model-based segmentation and elastic image registration. Med. Image Anal. 16(6), 1187–1201 (2012)
Bishop, C.M.: Neural networks for pattern recognition. Oxford University Press, Oxford (1995)
Braude, I., Marker, J., Museth, K., Nissanov, J., Breen, D.: Contour-based surface reconstruction using MPU implicit models. Graph. Models 69(2), 139–157 (2007)
de Bruijne, M., van Ginneken, B., Viergever, M.A., Niessen, W.J.: Interactive segmentation of abdominal aortic aneurysms in CTA images. Med. Image Anal. 8(2), 127–138 (2004)
Bürger, F., Buck, C., Luther, W., Pauli, J.: Image-based object classification of defects in steel using data-driven machine learning optimization. In: Proceedings of VISAPP 2014 - International Conference on Computer Vision Theory and Applications, Scitepress, pp. 143–152 (2014)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)
Freedman, D., Zhang, T.: Active contours for tracking distributions. IEEE Trans. Image Process. 13(4), 518–526 (2004)
Kovács, T.: Automatic segmentation of the vessel lumen from 3D CTA images of aortic dissection. Diss., Eidgenössische Technische Hochschule ETH Zürich, Nr. 19167 (2010)
Lankton, S., Tannenbaum, A.: Localizing region-based active contours. IEEE Trans. Image Process. 17(11), 2029–2039 (2008)
Lubniewski, P.J., Miguel, B., Sauvage, V., Lohou, C.: Interactive 3D segmentation by tubular envelope model for the aorta treatment. In: IS&T/SPIE Electronic Imaging, pp. 82901F–82901F-14. International Society for Optics and Photonics (2012)
Michailovich, O., Rathi, Y., Tannenbaum, A.: Image segmentation using active contours driven by the Bhattacharyya gradient flow. IEEE Trans. Image Process. 16(11), 2787–2801 (2007)
Moon, M.C., Greenberg, R.K., Morales, J.P., Martin, Z., Lu, Q., Dowdall, J.F., Hernandez, A.V.: Computed tomography-based anatomic characterization of proximal aortic dissection with consideration for endovascular candidacy. J. Vasc. Surg. 53(4), 942–949 (2011)
Morariu, C.A., Dohle, D.S., Terheiden, T., Tsagakis, K., Pauli, J.: Polar-based aortic segmentation in 3D CTA dissection data using a piecewise constant curvature model. Bildverarbeitung für die Medizin 2014, pp. 390–395. Springer, Heidelberg (2014)
Ohtake, Y., Belyaev, A., Alexa, M., Turk, G., Seidel, H.P.: Multi-level partition of unity implicits. ACM Trans. Graph. 22(3), 463–470 (2003)
Osher, s, Fedkiw, R.: Level set methods and dynamic implicit surfaces. Applied Mathematical Sciences, vol. 153. Springer, New York (2003)
Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness & correlation. J. Mach. Learn. Technol. 2(1), 37–63 (2011)
Sethian, J.A.: Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science, vol. 3. Cambridge University Press, Cambridge (1999)
Wang, X.F., Huang, D.S., Xu, H.: An efficient local Chan-Vese model for image segmentation. Pattern Recogn. 43(3), 603–618 (2010)
Wendt, D., Thielmann, M., Melzer, A., Benedik, J., Droc, I., Tsagakis, K., Dohle, D.S., Jakob, H., Abele, J.E.: The past, present and future of minimally invasive therapy in endovascular interventions: A review and speculative outlook. Minim. Invasive Ther. Allied Technol. 22(4), 242–253 (2013)
Wörz, S., von Tengg-Kobligk, H., Henninger, V., Rengier, F., Schumacher, H., Böckler, D., Kauczor, H.U., Rohr, K.: 3-D quantification of the aortic arch morphology in 3-D CTA data for endovascular aortic repair. IEEE Trans. Biomed. Eng. 57(10), 2359–2368 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Morariu, C.A., Terheiden, T., Dohle, D.S., Tsagakis, K., Pauli, J. (2014). Graph-Based and Variational Minimization of Statistical Cost Functionals for 3D Segmentation of Aortic Dissections. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_42
Download citation
DOI: https://doi.org/10.1007/978-3-319-11752-2_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11751-5
Online ISBN: 978-3-319-11752-2
eBook Packages: Computer ScienceComputer Science (R0)