Deep Learning-based Framework for Segmentation and Motion Tracking of Left Ventricle in 3D Echocardiography

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PROJECT SUMMARY/ABSTRACT Coronary artery disease remains the leading cause of death around the world. Acute myocardial infarction (MI) causes regional dysfunction which places remote areas of the heart at a mechanical disadvantage resulting in long term adverse left ventricular (LV) remodeling and complicated congestive heart failure (CHF). Stress echocardiography is currently the clinically established, cost-effective 2D imaging technique for detecting and characterizing myocardial injury by imaging the left ventricle at rest and after either exercise or pharmacologically-induced stress to reveal ischemia and/or infarct. However, the inherent limitations of a 2D echocardiography make it difficult to characterize the whole 3D volume of ischemic/infarct zone, and the qualitative assessment of wall-motion abnormality to characterize myocardial deformation leads to variability among experts. Although 3D echocardiography has potential to address the limitations of 2D imaging, it is not widely accepted in standard clinical use due to the low signal-to-noise ratio (SNR). With the recent advancements in deep learning algorithms, many segmentation and registration tasks have achieved near expert level accuracy. Also, previous works have shown the utility of strain analysis as a way to quantify the degree of wall-motion abnormality in cardiac imaging modalities. Still, many of the current deep learning frameworks focus largely on intensity-based features which are still difficult to train on 3D echocardiography datasets, which in turn leads to poor strain analysis. Thus, in this fellowship, I propose to develop novel data-driven neural network models specifically tailored to 3D echocardiography to improve segmentation and motion tracking of left ventricle in order to achieve full 3D cardiac strain analysis. My first aim is to develop a multi-frame attention-based neural network to exploit the spatiotemporal features of the echocardiography dataset to improve 3D segmentation of left ventricle. This method will take advantage of the inter-frame spatiotemporal features to augment the relevant feature extractions for segmentation. My second aim is to develop a registration neural network in 3D echocardiography by combining intensity-based features and surface-curvature bending energy to improve the motion tracking of left ventricle. This neural network will build upon the accurate segmentations from the first aim to include unique curvature energy features at the boundaries to enhance tracking accuracy at all areas of the myocardium. The improved motion tracking will be used to calculate strain for detection of full 3D ischemic/infarct zones. In summary, this research will provide an objective, quantitative tools for characterizing wall-motion abnormality with strain analysis in 3D echocardiography.