Abstract
The ability to identify and quantify two (2D) and three-dimensional (3D) morphological parameters of the aortic valve (AV) apparatus from transesophageal echocardiographic (TEE) imaging constitutes a valuable tool in diagnosis, treatment and follow-up of patients with aortic valve related diseases, as well as image-based morphological assessment for surgical interventions, so there is a considerable need to develop a standardized frameworks for 2D-3D valve segmentation and shape representation.
AV borders and leaflets quantification is still a challenging task, and commonly based on intensive user interaction that limits its applicability. We propose a fast and accurate model free, automated method for segmenting and extracting morphological parameters. This work integrates level-set techniques to automatically delineate and quantitatively describe aortic geometry in echocardiographic images, a challenging task that has been explored only to a limited extent. The algorithm accuracy was tested on 5 patients compared to “gold standard” manual analysis, showing strong agreement between both. The proposed technique appears promising for clinical application.
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References
Lozano, R., Naghavi, M., Foreman, K., et al.: Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380, 2095–2128 (2010)
Villines, et al.: Prevalence and severity of coronary artery disease and adverse events among symptomatic patients with coronary artery calcification scores of zero undergoing coronary computed tomography angiography: results from the CONFIRM (Coronary CT Angiography Evaluation for Clinical Outcomes: an International Multicenter) registry. J. Am. Coll. Cardiol. 58, 2533–2540 (2011)
Freeman, R.V., Otto, C.M.: Spectrum of Calcific Aortic Valve Disease Pathogenesis, Disease Progression, and Treatment Strategies. Circulation 111, 3316–3326 (2005). doi:10.1161/CIRCULATIONAHA.104.486738
Budoff, M.J.: Interpreting the Coronary-Artery Calcium Score. N. Engl. J. Med. 366, 1550–1551 (2012)
Otto, C.M., Lind, B.K., Kitzman, D.W., et al.: Association of aortic-valve sclerosis with cardiovascular mortality and morbidity in the elderly. N. Engl. J. Med. 341, 142 (1999)
Stewart, B.F., Siscovick, D., Lind, B.K., et al.: Clinical factors associated with calcific aortic valve disease. Cardiovascular Health Study. J. Am. Coll. Cardiol. 29, 630 (1997)
Gharacholou, S.M., Karon, B.L., Shub, C., Pellikka, P.A.: Aortic valve sclerosis and clinical outcomes: moving toward a definition. Am. J. Med. 124, 103 (2011)
Weyman, A.E.: Principles and Practice of ECHOCARDIOGRAPHY, 2nd edn.
Zhou, X.S., Georgescu, B.: Intelligent Computer Aided Interpretation in Echocardiography: Clinical Needs and recent Advances. In: Principles and Advanced Methods in Medical Imaging and Image Analysis, ch. 29, pp. 725–743
Alison Noble, J., Boukerroui, D.: Ultrasound Image Segmentation: A Survey. IEEE Transactions on Medical Imaging 25(8), 987–1010 (2006)
Ibánez, L., Schroeder, W., Ng, L., Cates, J.: The ITK Software Guide, 2nd edn. Kitware, Inc. (2005), http://www.itk.org/ItkSoftwareGuide.pdf, ISBN 1-930934-15-7
Schroeder, W., Martin, K., Lorensen, W.: The Visualization Toolkit: An Object Oriented Approach to Computer Graphics, 3rd edn. Kitware, Inc. (2004) http://www.vtk.org
Platz, E., Solomon, S.D.: Point-of-Care Echocardiography in the Accountable Care Organization Era. Advances in Cardiovascular Imaging
Bland, J.M., Altman, D.G.: Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 327(8476), 307–310 (1986)
Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79, 12–49 (1988)
Kimmel, R., Caselles, V., Sapiro, G.: Geodesic active contours. International Journal on Computer Vision 22(1), 61–97 (1997)
Lin, N., Yu, W., Duncan, J.S.: Combinative multi-scale level set framework for echocardiographic image segmentation. Med. Image Anal. 7(4), 529–537 (2003)
Leventon, M., Grimson, W., Faugeras, O.: Statistical shape influence in geodesic active contours. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 316–323 (2000)
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Veiga, C., Calvo, F., Paredes-Galán, E., Pazos, P., Peña, C., Íñiguez, A. (2014). An Automated Level-set Approach for Identification of Aortic Valve Borders in Short Axis Windows of Transesophageal Echo Sequences (TEE). In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science(), vol 8815. Springer, Cham. https://doi.org/10.1007/978-3-319-11755-3_25
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DOI: https://doi.org/10.1007/978-3-319-11755-3_25
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