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Simultaneous analysis of 2D echo views for left atrial segmentation and disease quantification

University of British Columbia

We propose a joint information framework for automatic analysis of 2D echocardiography (echo) data. The analysis combines a priori images, their segmentations and patient diagnostic information within a unified framework to determine various clinical parameters, such as cardiac chamber volumes, and cardiac disease labels. The main idea behind the framework is to employ joint Independent Component Analysis of both echo image intensity information and corresponding segmentation labels to generate models that jointly describe the image and label space of echo patients on multiple apical views jointly, instead of independently. These models are then both used for segmentation and volume estimation of cardiac chambers such as the left atrium and for detecting pathological abnormalities such as mitral regurgitation. We validate the approach on a large cohort of echos obtained from 6,993 studies. We report performance of the proposed framework in estimation of the left-atrium volume and diagnosis of mitral-regurgitation severity. A correlation coefficient of 0.87 was achieved for volume estimation of the left atrium when compared to the clinical report. Moreover, we classified patients that suffer from moderate or severe mitral regurgitation diagnosis with an average accuracy of 82%. Using only B-Mode echo information to automatically derive these clinical parameters, there is potential for this approach to be used clinically.

Text

2017-01-31

Attribution-NonCommercial-NoDerivs 2.5 Canada

http://hdl.handle.net/2429/56228

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/