Automating 3D echocardiographic image analysis

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%.