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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

TOWARDS FAST HARD-CONSTRAINED PARALLEL TRANSMIT DESIGN IN ULTRAHIGH FIELD MRI WITH PHYSICS-DRIVEN NEURAL NETWORKS.

Proceedings. IEEE International Symposium on Biomedical Imaging·2025
Same author

Surrogate Modeling for Bayesian Optimization Beyond a Single Gaussian Process.

IEEE transactions on pattern analysis and machine intelligence·2023
Same author

Incremental Ensemble Gaussian Processes.

IEEE transactions on pattern analysis and machine intelligence·2022
Same author

Graph-based Learning under Perturbations via Total Least-Squares.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society·2021
Same author

Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning.

IEEE transactions on pattern analysis and machine intelligence·2020
Same author

Efficient and Stable Graph Scattering Transforms via Pruning.

IEEE transactions on pattern analysis and machine intelligence·2020

Related Experiment Video

Updated: Dec 31, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

707

FULLY AUTOMATIC SEGMENTATION OF THE RIGHT VENTRICLE VIA MULTI-TASK DEEP NEURAL NETWORKS.

Liang Zhang1, Georgios Vasileios Karanikolas1, Mehmet Akçakaya1

  • 1Digital Tech. Center and Dept. of ECE, Univ. of Minnesota, Minneapolis, MN 55455, USA.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)
|January 2, 2020
PubMed
Summary

A new multi-task deep neural network improves cardiac magnetic resonance (MR) image segmentation for the right ventricle (RV). This approach enhances the prognosis of cardiac pathologies, especially for smaller RVs.

Keywords:
Right ventricle segmentationU-netconvolutional neural networksmulti-task learning

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.4K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K

Related Experiment Videos

Last Updated: Dec 31, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

707
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.4K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K

Area of Science:

  • Medical imaging analysis
  • Cardiovascular imaging
  • Artificial intelligence in medicine

Background:

  • Cardiac magnetic resonance (MR) imaging is crucial for diagnosing heart conditions.
  • Accurate segmentation of cardiac ventricles, particularly the right ventricle (RV), is essential for clinical parameter extraction and patient prognosis.
  • Existing fully convolutional network (FCN) methods for RV segmentation have limitations.

Purpose of the Study:

  • To propose a novel multi-task deep neural network (DNN) architecture for enhanced automatic right ventricle (RV) segmentation from cardiac MR images.
  • To leverage shared features across tasks for improved segmentation performance.
  • To provide a more accurate tool for cardiac pathology prognosis.

Main Methods:

  • Development and implementation of a multi-task U-net architecture using the Tensorflow framework.
  • The proposed DNN can utilize any FCN as a base.
  • The model is designed for efficient end-to-end training.

Main Results:

  • The multi-task DNN demonstrated improved segmentation performance compared to existing methods.
  • The approach showed particular effectiveness in segmenting small-sized right ventricles (RVs).
  • Numerical tests on real cardiac MR datasets validated the proposed method's capabilities.

Conclusions:

  • The proposed multi-task DNN architecture offers a significant advancement in automatic RV segmentation from cardiac MR images.
  • This enhanced segmentation accuracy, especially for small RVs, can lead to better clinical parameter assessment and improved cardiac pathology prognosis.
  • The flexible multi-task framework allows for integration with various FCNs, paving the way for future developments in cardiovascular image analysis.