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

N-Acetylcysteine protects the developing brain in neonatal sepsis-like inflammation via a redox-neurovascular pathway.

Journal of neuroinflammation·2026
Same author

Twisted Intramolecular Charge Shuttle (TICS) Enables Ultrafast Ratiometric Sensing and Imaging of Nitrite.

Angewandte Chemie (International ed. in English)·2026
Same author

Tackling antibiotic resistance in ESKAPE pathogens through the lens of bacterial small RNAs.

Microbial pathogenesis·2026
Same author

Factors influencing the decision to opt out of basic medical insurance among China's migrant population: a logistic regression analysis.

Frontiers in public health·2026
Same author

Dual Graph Strategy with Diffusion Tensor Imaging for Autism Spectrum Disorder Diagnosis.

IEEE transactions on bio-medical engineering·2026
Same author

Endovascular Treatment in Subtypes of Posterior Large Vessel Occlusion: A Pooled Analysis From 2 Registries.

Stroke (Hoboken, N.J.)·2026

Related Experiment Video

Updated: Dec 22, 2025

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

3.2K

Image Projection Network: 3D to 2D Image Segmentation in OCTA Images.

Mingchao Li, Yerui Chen, Zexuan Ji

    IEEE Transactions on Medical Imaging
    |May 5, 2020
    PubMed
    Summary

    We developed an Image Projection Network (IPN) for 3D-to-2D OCTA image segmentation. This novel approach effectively segments retinal vessels and foveal avascular zones without prior layer segmentation.

    More Related Videos

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

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    697
    A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
    09:41

    A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

    Published on: May 20, 2016

    12.6K

    Related Experiment Videos

    Last Updated: Dec 22, 2025

    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

    3.2K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    697
    A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
    09:41

    A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

    Published on: May 20, 2016

    12.6K

    Area of Science:

    • Ophthalmology
    • Medical Imaging
    • Computer Vision

    Background:

    • Optical Coherence Tomography Angiography (OCTA) is crucial for diagnosing retinal diseases.
    • Accurate segmentation of retinal structures like vessels and the foveal avascular zone (FAZ) is essential for quantitative analysis.
    • Current methods often require complex pre-processing or layer segmentation.

    Purpose of the Study:

    • To introduce a novel end-to-end 3D-to-2D image segmentation network, the Image Projection Network (IPN).
    • To enable direct segmentation of retinal indicators from 3D OCTA data without requiring retinal layer segmentation or projection maps.
    • To evaluate the IPN's performance on retinal vessel (RV) and foveal avascular zone (FAZ) segmentation tasks.

    Main Methods:

    • Developed an Image Projection Network (IPN) featuring a Projection Learning Module (PLM).
    • The PLM utilizes a unidirectional pooling layer for concurrent feature selection and dimension reduction.
    • The network processes 3D OCTA volumes to produce 2D segmentation outputs.

    Main Results:

    • The IPN achieved effective 3D-to-2D segmentation for both retinal vessel (RV) and foveal avascular zone (FAZ).
    • Experimental results on 316 OCTA volumes validated the IPN's performance.
    • The IPN outperformed baseline methods by leveraging multi-modality and volumetric information.

    Conclusions:

    • The IPN offers a new, simplified approach for quantifying retinal indicators from OCTA images.
    • This network provides an effective 3D-to-2D segmentation strategy for retinal imaging.
    • The IPN demonstrates significant potential for advancing OCTA-based retinal diagnostics.