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Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
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Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression

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Automated endocardial cushion segmentation and cellularization quantification in developing hearts using optical

Shan Ling¹, Jiawei Chen¹, Maryse Lapierre-Landry¹

¹Department of Biomedical Engineering, School of Engineering and School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.

Biomedical Optics Express

|February 3, 2023

Summary

This summary is machine-generated.

This study introduces an automated deep learning method to analyze endocardial cushion development in congenital heart defects (CHDs). The new technique rapidly quantifies cushion anatomy, revealing previously unreported spatial asymmetries in developing heart structures.

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Area of Science:

Cardiovascular Research
Developmental Biology
Medical Imaging

Background:

Congenital heart defects (CHDs), particularly valve and septal anomalies, represent a significant disease burden.
Endocardial cushions, crucial for heart valve and septum formation, develop through complex cellular processes.
Current manual analysis of cushion development is laborious and limits large-scale studies.

Purpose of the Study:

To develop and validate an automated strategy for characterizing endocardial cushion anatomy from optical coherence tomography (OCT) images.
To enable rapid, quantitative analysis of cushion development for large cohort studies.
To investigate spatial organization within endocardial cushions during heart development.

Main Methods:

A two-step deep learning model was employed for heart localization and endocardial cushion segmentation in OCT images.
K-means clustering was utilized to differentiate acellular and cellular regions within the cushions.
The automated method quantifies cushion volume, cellularity, and 3D spatial distribution.

Main Results:

The automated method successfully segmented endocardial cushions and quantified their development.
Analysis revealed a novel spatial asymmetry in acellular cardiac jelly within developing endocardial cushions.
This finding provides new insights into the complex morphogenetic processes of early heart development.

Conclusions:

Automated analysis of OCT images offers a powerful, efficient tool for studying endocardial cushion development.
The discovery of spatial asymmetry highlights previously unrecognized heterogeneity in cushion composition.
This approach can advance our understanding of CHD etiology and inform future therapeutic strategies.