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

Anatomy of the Eyeball

Anatomy of the Eyeball

The eye is a spherical, hollow structure composed of three tissue layers. The outer layer — the fibrous tunic, comprises the sclera — a white structure — and the cornea, which is transparent. The sclera encompasses some of the ocular surface, most of which is not visible. However, the 'white of the eye' is distinctively visible in humans compared to other species. The cornea, a clear covering at the front of the eye, enables light penetration. The eye's middle...

You might also read

Related Articles

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

Sort by

Same author

Deep learning-based monitoring and localization in autonomous underwater vehicle networks for aquatic ecosystem analysis.

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering·2026

Same author

An Enhancement on Convolutional Artificial Intelligent Based Diagnosis for Skin Disease Using Nanotechnology Sensors.

Computational intelligence and neuroscience·2022

Same author

A Smart Solution for Cancer Patient Monitoring Based on Internet of Medical Things Using Machine Learning Approach.

Evidence-based complementary and alternative medicine : eCAM·2022

Same author

Implementation of Artificial Neural Network to Predict Diabetes with High-Quality Health System.

Computational intelligence and neuroscience·2022

Same author

An Artificial Intelligence-Based Reactive Health Care System for Emotion Detections.

Computational intelligence and neuroscience·2022

Same author

Healthcare Biclustering-Based Prediction on Gene Expression Dataset.

BioMed research international·2022

Same journal

Evaluating a Novel Cell-Free Preservation Solution for Human Cardiomyocyte Protection: A Proof-of-Concept Study.

BioMed research international·2026

Same journal

Clinical Efficacy of Chinese Medicine in Treating Adult Henoch-Schönlein Purpura: A Meta-Analysis.

BioMed research international·2026

Same journal

RETRACTION: Rehabilitation Training and Resveratrol Improve the Recovery of Neurological and Motor Function in Rats after Cerebral Ischemic Injury through the Sirt1 Signaling Pathway.

BioMed research international·2026

Same journal

The Oncogenic and Tumor-Suppressive Roles of SNHG18: A Double-Edged Long Noncoding RNA in Cancer.

BioMed research international·2026

Same journal

Evaluation of LncRNA NEAT1 and MEG3 Expression Levels in Hospitalized COVID-19 Patients.

BioMed research international·2026

Same journal

Perceived Self-Efficacy and Its Determinants for Noncommunicable Disease Prevention Among Adults in Southern Ethiopia: A Community-Based Cross-Sectional Study.

BioMed research international·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 23, 2025

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

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

Published on: November 30, 2022

Segmentation of Optic Disc and Cup Using Modified Recurrent Neural Network.

J Surendiran¹, S Theetchenya², P M Benson Mansingh³

¹Department of Electronics and Communication Engineering, HKBK College of Engineering, India.

Biomed Research International

|May 13, 2022

Summary

This summary is machine-generated.

This study introduces a modified recurrent neural network (mRNN) for accurate optic disc and cup segmentation in eye images. The mRNN method significantly improves segmentation rates for diagnosing glaucoma, a leading cause of vision loss.

More Related Videos

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Microdissection of the Rodent Eye

Microdissection of the Rodent Eye

Published on: April 21, 2023

Related Experiment Videos

Last Updated: Sep 23, 2025

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

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

Published on: November 30, 2022

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Microdissection of the Rodent Eye

Microdissection of the Rodent Eye

Published on: April 21, 2023

Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Glaucoma is a primary cause of irreversible vision loss.
Accurate cup-to-disc ratio measurement is crucial for glaucoma diagnosis.
Automated segmentation of optic disc and cup aids in clinical assessment.

Purpose of the Study:

To develop an advanced deep learning model for optic disc and cup segmentation.
To enhance the accuracy of glaucoma diagnosis through improved image analysis.
To evaluate the efficacy of the proposed modified recurrent neural network (mRNN).

Main Methods:

Developed a modified recurrent neural network (mRNN) combining recurrent neural network (RNN) and fully convolutional network (FCN).
The mRNN model leverages intra- and interslice contextual information for feature extraction.
Simulations were conducted to assess the segmentation performance on eye images.

Main Results:

The proposed mRNN model demonstrated superior segmentation performance compared to existing deep learning models.
The integration of contextual information by mRNN optimized the segmentation of optic cup and disc.
The method achieved improved segmentation rates on datasets including Drive, STARE, MESSIDOR, ORIGA, and DIARETDB.

Conclusions:

The mRNN model offers an efficient and effective approach for optic disc and cup segmentation.
This technique holds significant potential for improving the early detection and management of glaucoma.
The study highlights the benefits of incorporating contextual information in deep learning for medical image analysis.