Victor Lempitsky, Michael Verhoek, Alison Noble, and Andrew Blake
Automatic delineation of the myocardium in real-time 3D echocardiography may be used to aid the diagnosis of heart problems such as ischaemia, by enabling quantification of wall thickening and wall motion abnormalities. Distinguishing between myocardial and nonmyocardial tissue is, however, difficult due to low signal-to-noise ratio as well as the efficiency constraints imposed on any algorithmic solution by the large size of the data under consideration. In this paper, we take a machine learning approach treating this problem as a two-class 3D patch classification task. We demonstrate that solving such task using random forests, which are the discriminative classifiers developed recently in the machine learning community, allows to obtain accurate delineations in a matter of seconds (on a CPU) or even in real-time (on a GPU) for the entire 3D volume.
|Published in||FIMH 2009 [best paper award]|
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