Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Nov 3, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

733

A Convolutional Neural Network Combining Discriminative Dictionary Learning and Sequence Tracking for Left

Xuchu Wang1, Fusheng Wang1, Yanmin Niu2

  • 1Key Laboratory of Optoelectronic Technology and Systems of Ministry of Education, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China.

Sensors (Basel, Switzerland)
|June 2, 2021
PubMed
Summary

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

Genome-Wide Identification and Functional Characterization of Dof Transcription Factors Involved in Salt Stress Responses in Taraxacum kok-saghyz.

Physiologia plantarum·2026
Same author

APASL clinical practice guidance: the diagnosis and management of patients with Wilson's disease.

Hepatology international·2026
Same author

Prediction of Coal Spontaneous Combustion Risk under Poor Oxygen Concentration Based on Fuzzy Clustering and Lattice Tightness.

ACS omega·2026
Same author

Thermodynamic-Kinetic Tailored Photothermal-Responsive Molecular Switching for Extracellular Vesicle Manipulation.

ACS nano·2026
Same author

Multi-omics analysis identifies key genes and functional loci affecting teat number in American Large White and Landrace pigs and their application in optimizing genomic selection models.

BMC genomics·2026
Same author

Fragmentation Analysis of the MYC Enhancer in Cell-Free DNA by qPCR for Early Detection of Hepatocellular Carcinoma.

Hepatology research : the official journal of the Japan Society of Hepatology·2026

This study introduces a novel convolutional neural network (CNN) for left ventricular (LV) detection in cardiac MRI, improving accuracy for heart disease diagnosis. The method enhances LV area adaptability, crucial for precise cardiac image processing.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Cardiovascular Disease Diagnosis

Background:

  • Left ventricular (LV) detection in cardiac MRI is vital for computer-aided diagnosis of heart diseases.
  • Challenges include large LV spans, varying sizes, and heterogeneous tissue/blood pool composition within the LV area.
  • Existing methods struggle with adaptability to these variations.

Purpose of the Study:

  • To propose a novel convolutional neural network (CNN) detection method for accurate left ventricular (LV) detection in cardiac MRI.
  • To address the challenges of varying LV sizes and heterogeneous areas.
  • To improve the efficiency and adaptability of LV detection in cardiac image processing.

Main Methods:

  • A CNN-based detection method combining discriminative dictionary learning and sequence tracking.
Keywords:
convolutional neural networkcorrelation filteringdiscriminative dictionary learningleft ventricular detectionscale adaptive anchorssuperpixel segmentation

More Related Videos

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
08:10

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Published on: July 20, 2022

1.9K

Related Experiment Videos

Last Updated: Nov 3, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

733
Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
08:10

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Published on: July 20, 2022

1.9K
  • Superpixel oversegmentation with discriminant dictionary for region classification.
  • Label merging and multi-scale adaptive anchors for target LV region construction and size variation handling.
  • CNN-based regression and classification for LV localization.
  • A fast generation module for scale-adaptive anchors using sequence tracking to improve speed.

    • Main Results:

    • The proposed method achieved a 92.95% AP50 metric on the heart atlas dataset, outperforming typical related methods.
    • Experimental results verified the effectiveness and adaptability of the combined discriminative dictionary learning and scale adaptive anchor approach.
    • The method demonstrated improved adaptability to varying left ventricular areas.

    Conclusions:

    • The proposed CNN method effectively addresses challenges in left ventricular (LV) detection in cardiac MRI.
    • The combination of discriminative dictionary learning and scale adaptive anchors enhances algorithm adaptability.
    • This approach is beneficial for cardiac image processing tasks like region-of-interest cropping and LV volume measurement.