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

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

1.1K
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
1.1K
Association Areas of the Cortex01:21

Association Areas of the Cortex

6.8K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
6.8K

You might also read

Related Articles

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

Sort by
Same author

Exploring the iodine adsorption and deposition behavior: An out-of-pile experiment on accident conditions of lead-bismuth fast reactor.

Journal of hazardous materials·2025
Same author

Predictive stability in biopharmaceuticals and vaccines: Perspectives and recommendations towards accelerating patient access.

Journal of pharmaceutical sciences·2025
Same author

Recombinant Type III Humanized Collagen Solution for Injection Promotes Skin Repair in Chinese Population: A Case Series.

Journal of cosmetic dermatology·2025
Same author

Three new terpenes with potential antibacterial and anti-COVID-19 activities from the stems of <i>Eurya chinensis</i> R. Br.

Natural product research·2025
Same author

Deposition behavior of PbTe doped LBE aerosol and Te valence prediction: Platform test and First-principles calculation.

Journal of hazardous materials·2024
Same author

FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-Supervised Medical Image Segmentation.

IEEE transactions on medical imaging·2024

Related Experiment Video

Updated: Oct 10, 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

3.0K

Multi-task Learning Based Ocular Disease Discrimination and FAZ Segmentation Utilizing OCTA Images.

Zhonghua Wang, Li Lin, Jiewei Wu

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
    PubMed
    Summary

    This study introduces a deep learning method for segmenting the foveal avascular zone (FAZ) and classifying ocular diseases from OCTA images. The approach effectively uses shared features for both tasks, aiding in disease screening.

    More Related Videos

    Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
    12:22

    Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

    Published on: August 4, 2018

    8.7K
    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
    07:11

    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

    Published on: December 8, 2023

    2.0K

    Related Experiment Videos

    Last Updated: Oct 10, 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

    3.0K
    Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
    12:22

    Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

    Published on: August 4, 2018

    8.7K
    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
    07:11

    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

    Published on: December 8, 2023

    2.0K

    Area of Science:

    • Ophthalmology
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Accurate segmentation of the foveal avascular zone (FAZ) is crucial for diagnosing ocular diseases.
    • Current methods may lack efficiency in simultaneously analyzing FAZ structure and disease states.

    Purpose of the Study:

    • To develop and validate a multi-task deep learning model for simultaneous FAZ segmentation and ocular disease classification (normal, diabetic, myopia).
    • To leverage shared features between segmentation and classification tasks for improved diagnostic accuracy.

    Main Methods:

    • A cotraining network structure was designed, sharing an encoder between FAZ segmentation and ocular disease classification networks.
    • A classification head was integrated into the segmentation network's encoder to utilize extracted features.

    Main Results:

    • The model achieved high performance in FAZ segmentation with Dice (0.9031±0.0772) and Jaccard (0.8302 ±0.0990) indices.
    • The classification task yielded an Accuracy of 0.7533 and Kappa of 0.6282 for distinguishing between normal, diabetic, and myopia states.
    • Validation was performed on the FAZID dataset.

    Conclusions:

    • The proposed multi-task deep learning method offers a robust tool for simultaneous FAZ segmentation and ocular disease classification using OCTA images.
    • This approach holds significant clinical potential for early ocular disease screening and biomarker identification.