Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A multimodal data acquisition and registration system for monitoring skin wounds: Wound modeling and accuracy estimation using phantoms.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

Same author

Objective Dynamic Assessment of Facial Movement Asymmetry in Children Using a Marker-Based Video Method.

Journal of clinical medicine·2026

Same author

Initial evaluation of a mixed-reality system for image-guided navigation during percutaneous liver tumor ablation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026

Same author

Metallic Artifacts' Reduction in Microtomography Using the Bone- and Soft-Tissue Decomposition Method.

Sensors (Basel, Switzerland)·2024

Same author

Physiological response to pain in patients with temporomandibular joint disorders during physiotherapy.

Scientific reports·2024

Same author

An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition.

Scientific data·2024

Same journal

Corrigendum to "CFPNet-M: A light-weight encoder-decoder based network for multimodal biomedical image real-time segmentation" [Comput. Biol. Med. 154 (2023) 106579].

Computers in biology and medicine·2026

Same journal

ECG arrhythmia classification via wavelet-driven feature extraction and swarm-optimised gradient boosting.

Computers in biology and medicine·2026

Same journal

Electro-osmotic metachronal cilia transport of viscoelastic blood infused with penta-hybrid nanoparticles in an oviduct: Analytical and neural network modeling.

Computers in biology and medicine·2026

Same journal

sEEGnal: an automated EEG preprocessing pipeline evaluated against expert-driven preprocessing.

Computers in biology and medicine·2026

Same journal

Corrigendum to "Integrating experimental biology, computational methods, and artificial Intelligence in anticancer drug discovery: Bridging the translational Gap" [Comput. Biol. Med. 213 (2026) 111832].

Computers in biology and medicine·2026

Same journal

Organ dose optimization for a point-of-care forearm X-ray photon-counting CT.

Computers in biology and medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 18, 2026

3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

Published on: May 19, 2015

Mathematical model in left ventricle segmentation.

Monika N Bugdol¹, Ewa Pietka¹

¹Faculty of Biomedical Engineering, Silesian University of Technology, Akademicka 16, Gliwice, Poland.

Computers in Biology and Medicine

|January 10, 2015

Summary

This summary is machine-generated.

This study introduces a parametric model to correct left ventricle segmentation errors. The model improves accuracy in calculating cardiac hemodynamic parameters and heart mass, reducing clinical errors.

Keywords:

Automatic segmentation Cardiac MRI Left ventricle Model-based segmentation Parametric model

More Related Videos

In Silico Clinical Trials for Cardiovascular Disease

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Displacement Analysis of Myocardial Mechanical Deformation DIAMOND Reveals Segmental Heterogeneity of Cardiac Function in Embryonic Zebrafish

Displacement Analysis of Myocardial Mechanical Deformation DIAMOND Reveals Segmental Heterogeneity of Cardiac Function in Embryonic Zebrafish

Published on: February 6, 2020

Related Experiment Videos

Last Updated: Apr 18, 2026

3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

Published on: May 19, 2015

In Silico Clinical Trials for Cardiovascular Disease

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Displacement Analysis of Myocardial Mechanical Deformation DIAMOND Reveals Segmental Heterogeneity of Cardiac Function in Embryonic Zebrafish

Displacement Analysis of Myocardial Mechanical Deformation DIAMOND Reveals Segmental Heterogeneity of Cardiac Function in Embryonic Zebrafish

Published on: February 6, 2020

Area of Science:

Cardiovascular imaging and modeling
Biomedical engineering
Medical image analysis

Background:

Accurate segmentation of the left ventricle myocardium is crucial for assessing cardiac function.
Existing segmentation algorithms can produce errors, particularly on specific cardiac phases like end-systole.
These errors impact the reliability of computed cardiac hemodynamic parameters and heart mass.

Purpose of the Study:

To present a parametric model for estimating left ventricle myocardium shape.
To correct segmentation inaccuracies on cardiac slices.
To enhance the accuracy of cardiac hemodynamic parameters and heart mass calculations.

Main Methods:

Development of a parametric model for left ventricle myocardium.
Application of the model to correct segmentation errors on cardiac slices.
Evaluation of the model's impact on cardiac parameter computation.

Main Results:

The parametric model effectively estimates myocardium shape even with incorrect segmentation.
The model significantly improved the accuracy of cardiac hemodynamic parameters and heart mass.
For the segmentation algorithm used, errors decreased from clinically unacceptable to acceptable levels.

Conclusions:

The proposed parametric model offers a robust solution for improving left ventricle segmentation accuracy.
This approach enhances the reliability of quantitative cardiac assessments, particularly for heart mass and hemodynamic parameters.
The model is compatible with various segmentation algorithms and shows greatest utility at end-systole.