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Imaging Studies for Cardiovascular System I:Echocardiography

Imaging Studies for Cardiovascular System I:Echocardiography

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.
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion,...

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Updated: Aug 28, 2025

2D and 3D Echocardiography in the Axolotl Ambystoma Mexicanum

2D and 3D Echocardiography in the Axolotl Ambystoma Mexicanum

Published on: November 29, 2018

An efficient annotated data generation method for echocardiographic image segmentation.

Patrice Monkam¹, Songbai Jin¹, Wenkai Lu¹

¹Easysignal Group, State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China.

Computers in Biology and Medicine

|September 17, 2022

Summary

This summary is machine-generated.

This study introduces a novel framework to generate synthetic echocardiography (echo) data, overcoming the need for extensive manual annotation. Pre-training deep learning models with this synthetic data significantly improves cardiac structure segmentation performance in cardiovascular disease analysis.

Keywords:

Annotated data generation Cardiac structure segmentation Echocardiography Performance improvement Transfer learning

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Last Updated: Aug 28, 2025

2D and 3D Echocardiography in the Axolotl Ambystoma Mexicanum

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Published on: November 29, 2018

Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging

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Area of Science:

Medical Imaging
Artificial Intelligence
Cardiovascular Diseases

Background:

Deep learning shows promise for echocardiography (echo) data segmentation in diagnosing cardiovascular diseases (CVDs).
Acquiring large, high-quality annotated echo datasets for deep learning is challenging due to time and expertise requirements.

Purpose of the Study:

To develop a framework for rapidly generating annotated echo data to circumvent manual annotation limitations.
To enable the deployment of deep learning models for echo data analysis.

Main Methods:

A two-phase framework was proposed for generating annotated echo data.
Phase 1: Simulated cardiac structures using polynomial fitting.
Phase 2: Embedded simulated structures onto endoscopic ultrasound images via Fourier Transform.

Main Results:

Generated annotated images were used as an auxiliary dataset to pre-train deep learning models.
Fine-tuning pre-trained models significantly improved segmentation performance compared to training from scratch.
Performance gains reached 12.9% in Dice and 7.74% in IoU.

Conclusions:

The proposed framework effectively addresses the shortage of labeled data in echo analysis.
It facilitates the wider application of deep learning in cardiovascular imaging.
The method shows great potential for advancing automated echo data analysis.