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

Molecular shielding in TiO<sub>2</sub> nanotubes: unlocking oxovanadium(IV) for ultrafast aqueous polymerization.

Chemical communications (Cambridge, England)·2026

Same author

Machine learning-integrated multi-objective application and optimization framework for sulfur-based reactive filler towards nutrient removal.

Water research·2026

Same author

Machine learning-driven prediction and control system for practical application of sulfur-based autotrophic denitrification biofilters.

Water research·2026

Same author

Natural variation of <i>CTS1</i> confers cold tolerance and blast resistance in rice.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

SPP1 improves early risk assessment of postpartum adverse events in pulmonary arterial hypertension complicated with pregnancy.

European journal of obstetrics, gynecology, and reproductive biology·2026

Same author

Spatially resolved nitrous oxide emissions in wastewater treatment processes.

Water research·2026

Same journal

Multimodal Contrastive Spatiotemporal Self-Organizing Neural Networks for In-Home Activity Learning of Mild Cognitive Impairment.

IEEE journal of biomedical and health informatics·2026

Same journal

Integrating Multi-View Residue Graph and Protein Language Model for Cell-Penetrating Peptide Prediction via Global-Local Graph Aggregation and Cross-Attentive Fusion.

IEEE journal of biomedical and health informatics·2026

Same journal

An Ultra-Lightweight Cross-scale Attention Mamba Network for Accurate Skin Lesion Segmentation.

IEEE journal of biomedical and health informatics·2026

Same journal

Explanation-Guided Reconstruction of Missing Clinical Features for Survival Prediction in Pancreatic Cancer.

IEEE journal of biomedical and health informatics·2026

Same journal

stDGCN: A dual-augmentation graph convolutional network for identifying spatial domains with attention mechanism.

IEEE journal of biomedical and health informatics·2026

Same journal

Patient-specific Biomechanical Investigation of Percutaneous Pulmonary Valves: Towards the Integration of Routinely Acquired Clinical Data and Fluid-structure Interaction Simulations.

IEEE journal of biomedical and health informatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 4, 2025

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Published on: February 18, 2015

Cardiac Adipose Tissue Segmentation via Image-Level Annotations.

Ziyi Huang, Yu Gan, Theresa Lye

IEEE Journal of Biomedical and Health Informatics

|April 6, 2023

Summary

This summary is machine-generated.

This study introduces a weakly supervised deep learning method for segmenting cardiac adipose tissue in optical coherence tomography (OCT) images. The approach uses image-level annotations, achieving performance comparable to fully supervised methods.

More Related Videos

Adipo-Clear: A Tissue Clearing Method for Three-Dimensional Imaging of Adipose Tissue

Adipo-Clear: A Tissue Clearing Method for Three-Dimensional Imaging of Adipose Tissue

Published on: July 28, 2018

Exploring Adipose Tissue Structure by Methylsalicylate Clearing and 3D Imaging

Exploring Adipose Tissue Structure by Methylsalicylate Clearing and 3D Imaging

Published on: August 19, 2020

Related Experiment Videos

Last Updated: Aug 4, 2025

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Published on: February 18, 2015

Adipo-Clear: A Tissue Clearing Method for Three-Dimensional Imaging of Adipose Tissue

Adipo-Clear: A Tissue Clearing Method for Three-Dimensional Imaging of Adipose Tissue

Published on: July 28, 2018

Exploring Adipose Tissue Structure by Methylsalicylate Clearing and 3D Imaging

Exploring Adipose Tissue Structure by Methylsalicylate Clearing and 3D Imaging

Published on: August 19, 2020

Area of Science:

Medical Imaging
Cardiovascular Research
Artificial Intelligence in Medicine

Background:

Accurate identification of cardiac structural substrates is crucial for guiding interventional procedures and optimizing treatment for complex arrhythmias like atrial fibrillation and ventricular tachycardia.
Optical coherence tomography (OCT) is a real-time imaging modality valuable for visualizing cardiac tissue.
Current cardiac image analysis heavily relies on fully supervised learning, requiring extensive pixel-wise annotations, which are labor-intensive and time-consuming.

Purpose of the Study:

To develop a novel two-stage deep learning framework for cardiac adipose tissue segmentation using only image-level annotations on OCT images.
To overcome the limitations of pixel-wise labeling in cardiac tissue segmentation.
To enable automatic tissue analysis with reduced annotation workload.

Main Methods:

A two-stage deep learning framework was developed for cardiac adipose tissue segmentation.
The framework integrates class activation mapping with superpixel segmentation to address challenges with sparse tissue seeds.
Weakly supervised learning techniques were employed, utilizing image-level annotations instead of pixel-wise labels.

Main Results:

The proposed weakly supervised approach demonstrated comparable performance to fully supervised methods trained on pixel-wise annotations.
The method effectively segments cardiac adipose tissue from human cardiac OCT images.
This study represents the first attempt at cardiac tissue segmentation on OCT images using weakly supervised learning.

Conclusions:

Weakly supervised learning with image-level annotations is a viable and effective approach for cardiac tissue segmentation in OCT images.
The developed framework significantly reduces the need for laborious pixel-wise annotations.
This method bridges the gap between the need for automatic cardiac tissue analysis and the availability of annotated data.