Abstract
3D echocardiography is a recent cardiac imaging method actively developed for quantitative analysis of heart function. A major barrier for its use as a quantitative tool in routine clinical practice is the absence of accurate and robust segmentation and tracking methods necessary to make the analysis fully automatic. In this article we present a fully-automated 3D echocardiographic image processing protocol for assessment of left ventricular (LV) function. We combine global image information provided by a novel multi-scale fuzzy-clustering segmentation algorithm, with local boundaries obtained with phase-based acoustic feature detection. We fit and track the LV surface using a 4D continuous transformation. To our knowledge this is the first report of a completely automated method. The protocol is viable for clinical practice. We exhibit and compare qualitative and quantitative results on three 3D image sequences that have been processed manually, in semi-automatic manner, and in fully automated fashion. Volume curves are derived and the ejection fractions errors with respect to manual segmentation are shown to be below 5%.
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Sanchez-Ortiz, G.I., Declerck, J., Mulet-Parada, M., Noble, J.A. (2000). Automating 3D Echocardiographic Image Analysis. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2000. MICCAI 2000. Lecture Notes in Computer Science, vol 1935. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-40899-4_71
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DOI: https://doi.org/10.1007/978-3-540-40899-4_71
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