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

> **NIH NIH F30** · YALE UNIVERSITY · 2022 · $31,607

## Abstract

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.

## Key facts

- **NIH application ID:** 10563111
- **Project number:** 5F30HL158154-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Shawn Ahn
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $31,607
- **Award type:** 5
- **Project period:** 2021-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10563111

## Citation

> US National Institutes of Health, RePORTER application 10563111, Deep Learning-based Framework for Segmentation and Motion Tracking of Left Ventricle in 3D Echocardiography (5F30HL158154-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10563111. Licensed CC0.

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