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

Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration.

Journal of medical Internet research·2026

Same author

The severity of post-infarction edema suggests a bimodal pattern, whereas the extent does not.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance·2026

Same author

Use of Z-Scores and Percentages to Assess Structural Brain MRI Findings in Patients With Schizophrenia.

International journal of methods in psychiatric research·2026

Same author

Native T1-mapping using cardiovascular magnetic resonance detects myocardium at risk during the first week following myocardial infarction in a swine model and in patients - comparison to contrast-enhanced cine steady-state free precession.

BMC cardiovascular disorders·2026

Same author

Radiomics-Based Model Using Tumor and Peritumoral Features with Semi-Supervised and Privileged Learning for Metastatic Risk Prediction in Lung Cancer: A Multi-Site Study.

Computer methods and programs in biomedicine·2025

Same author

Multi-center investigation of cardiac diffusion tensor imaging in healthy volunteers by the Society of Cardiovascular Magnetic Resonance Cardiac Diffusion Special Interest Group NETwork (SIGNET).

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance·2025

Search research articles

Related Experiment Video

Updated: Dec 1, 2025

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training

Published on: February 10, 2022

Simulator-generated training datasets as an alternative to using patient data for machine learning: An example in

Christos G Xanthis¹, Dimitrios Filos², Kostas Haris²

¹Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece; Department of Clinical Physiology, Clinical Sciences, Lund University and Lund University Hospital, Lund, Sweden.

Computer Methods and Programs in Biomedicine

|November 8, 2020

Summary

Advanced MR simulations create artificial datasets for machine learning, overcoming data limitations in medical imaging. This cost-effective approach trains neural networks without real MRI scans or personnel.

Keywords:

Magnetic resonance imaging machine learning segmentation simulation supervised techniques

More Related Videos

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Published on: January 18, 2021

MRI and PET in Mouse Models of Myocardial Infarction

MRI and PET in Mouse Models of Myocardial Infarction

Published on: December 19, 2013

Related Experiment Videos

Last Updated: Dec 1, 2025

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training

Published on: February 10, 2022

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Published on: January 18, 2021

MRI and PET in Mouse Models of Myocardial Infarction

MRI and PET in Mouse Models of Myocardial Infarction

Published on: December 19, 2013

Area of Science:

Medical Imaging
Machine Learning
Computational Anatomy

Background:

Supervised machine learning for MRI analysis requires extensive, representative, and well-labeled training data, which is often scarce or unavailable.
Limitations include data scarcity, small dataset size, lack of representativeness, and weak labels, hindering model development.

Purpose of the Study:

To develop a cost-effective method for generating artificial MRI training datasets using advanced simulations.
To overcome limitations associated with real-world MRI data acquisition for supervised learning.

Main Methods:

Utilized the 4D-XCAT model and coreMRI simulation platform to generate artificial short-axis MR images.
Trained a neural network for left ventricular (LV) endocardium and epicardium delineation using simulated and real MRI data.

Main Results:

Achieved high performance metrics (94% endocardium, 90% DICE epicardium) on real MRI data.
Incorporating 10% real MRI data into the artificial dataset boosted performance to 97% DICE.
Simulated data covering the entire LV, combined with real data (80%-20% mix), yielded 85% (endocardium) and 88% DICE (epicardium).

Conclusions:

Advanced MR simulations offer a low-cost solution for creating artificial training datasets.
This approach bypasses the need for real MRI scanners, patient scanning, and specialized personnel.
Enables robust supervised learning model development in medical image analysis.