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Related Experiment Video

Updated: Feb 28, 2026

3D Whole-heart Myocardial Tissue Analysis

3D Whole-heart Myocardial Tissue Analysis

Published on: April 12, 2017

Left ventricle Hermite-based segmentation.

Jimena Olveres¹, Rodrigo Nava², Boris Escalante-Ramírez¹

¹Facultad de Ingeniería, Universidad Nacional Autónoma de México, Mexico.

Computers in Biology and Medicine

|June 16, 2017

Summary

This summary is machine-generated.

This study introduces a new 2D technique for segmenting cardiac boundaries using computed tomography (CT) imaging. The method automates segmentation of heart cavities, aiding in faster and more accurate heart disease diagnosis.

Keywords:

Active shape models Left ventricle segmentation Level sets Local binary patterns Ray Feature error Steered Hermite transform

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Published on: May 19, 2015

Area of Science:

Medical Imaging
Biomedical Engineering
Cardiology

Background:

Computed tomography (CT) is crucial for cardiac imaging, but manual segmentation of heart cavities is time-consuming.
Accurate segmentation is vital for diagnosing conditions affecting heart function.

Purpose of the Study:

To develop a novel 2D technique for segmenting endocardium and epicardium boundaries in cardiac CT images.
To automate the segmentation process, reducing the time and effort required for cardiac diagnosis.

Main Methods:

A 2D approach utilizing the Hermite transform to compute information from the left ventricle and adjacent structures.
Integration of computed information with active shape models and level sets for enhanced segmentation.
Evaluation using Dice coefficient, Hausdorff distance, and a novel Ray Feature error metric.

Main Results:

The proposed method accurately discriminates cardiac tissue.
The technique demonstrates potential for improving the efficiency and accuracy of cardiac segmentation.
Quantitative assessment showed promising results using established and novel metrics.

Conclusions:

The novel segmentation technique offers a valuable tool for supporting heart disease diagnosis.
Automated segmentation can streamline the diagnostic workflow and aid in treatment planning.
This approach may enhance the clinical utility of cardiac CT imaging.