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

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

1.3K
Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...
1.3K
Reticular Dermis01:15

Reticular Dermis

4.7K
The papillary and reticular dermis are the two layers of the dermis. They are made of connective tissue with fibers of collagen extending from one to the other, making the border between the two somewhat indistinct. The dermal papillae extending into the epidermis belong to the papillary layer, whereas the dense collagen fiber bundles below belong to the reticular layer.
Reticular Layer
Underlying the papillary layer is the much thicker reticular layer, composed of dense, irregular connective...
4.7K
The Sense of Self: Reflected Self-Appraisal and Social Comparison02:57

The Sense of Self: Reflected Self-Appraisal and Social Comparison

56.1K
According to Charles Cooley, we base our image on what we think other people see (Cooley 1902). We imagine how we must appear to others, then react to this speculation. We don certain clothes, prepare our hair in a particular manner, wear makeup, use cologne, and the like—all with the notion that our presentation of ourselves is going to affect how others perceive us. We expect a certain reaction, and, if lucky, we get the one we desire and feel good about it. But more than that, Cooley...
56.1K
Functional Brain Systems: Reticular Formation01:13

Functional Brain Systems: Reticular Formation

4.9K
The reticular formation is a complex network of gray and white matter located within the brainstem extending from the medulla to the midbrain.
Within the reticular formation, there are several distinct nuclei that can be classified into three broad categories. The Raphe nuclei are located along the midline of the brainstem. They are primarily known for their role in synthesizing and releasing serotonin, a neurotransmitter involved in regulating mood, appetite, sleep, and circadian rhythms. The...
4.9K
Reflection of Waves01:07

Reflection of Waves

4.6K
When a wave travels from one medium to another, it gets reflected at the boundary of the second medium. A common example of this is when a person yells at a distance from a cliff and hears the echo of their voice. The sound waves (longitudinal waves) traveling in the air are reflected from the bounding cliff. Similarly, flipping one end of a string whose other end is tied to a wall causes a pulse (transverse wave) to travel through the string, which gets reflected upon reaching the wall. In...
4.6K
Leveling Effect01:29

Leveling Effect

1.4K
In acid-base chemistry, the leveling effect refers to the limitation imposed by the solvent on the strength of acids and bases in solution. When a base stronger than the solvent's conjugate base is used, it deprotonates the solvent until the base is entirely consumed, making it ineffective against weaker acids. Conversely, an acid stronger than the solvent's conjugate acid protonates the solvent until the acid is depleted, rendering it ineffective against weaker bases. Essentially, the...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Comparison of Measurement Techniques for Photoreceptor Loss in Geographic Atrophy.

Translational vision science & technology·2026
Same author

Domain Generalization Mitigates Scanner-Induced Domain Shift in Medical Imaging.

Journal of imaging informatics in medicine·2026
Same author

Comparison of Clarus, Optos, and Heidelberg Systems for Geographic Atrophy Area Measurements.

Ophthalmology science·2026
Same author

Application of a Quantitative Vascular Severity Score in Retinopathy of Prematurity in the United States and India: New Insights Into Disease Epidemiology and Pathophysiology.

American journal of ophthalmology·2026
Same author

Parafoveal Dark Adaptation in Early and Intermediate Age-Related Macular Degeneration.

Investigative ophthalmology & visual science·2026
Same author

Improving Efficiency in Geographic Atrophy Clinical Trials Using Run-In Phases or Single-Arm Designs.

JAMA ophthalmology·2026

Related Experiment Video

Updated: Feb 4, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.8K

ReticularNet: Automated Pixel-Level Segmentation of Reticular Pseudodrusen on Near-Infrared Reflectance Images by

Souvick Mukherjee1, Dylan Wu1, Leon von der Emde1

  • 1Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.

Ophthalmology Science
|February 2, 2026
PubMed
Summary

A deep learning model, ReticularNet, provides automated pixel-level grading of reticular pseudodrusen (RPD) on near-infrared reflectance (NIR) images. Its performance surpasses human graders, offering improved quantitative analysis for age-related macular degeneration (AMD) research.

Keywords:
Age-related macular degenerationDeep learningNear-infrared reflectance imagingReticular pseudodrusenSubretinal drusenoid deposits

More Related Videos

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

3.5K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.9K

Related Experiment Videos

Last Updated: Feb 4, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.8K
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

3.5K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.9K

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Reticular pseudodrusen (RPD) are key biomarkers in age-related macular degeneration (AMD).
  • Current grading methods for RPD are often qualitative and lack spatial or quantitative analysis.
  • Accurate RPD assessment is crucial for understanding AMD progression.

Purpose of the Study:

  • To develop and validate a deep learning model for pixel-level RPD grading using near-infrared reflectance (NIR) images.
  • To enable quantitative and spatial analysis of RPD burden.
  • To improve the accuracy and efficiency of RPD assessment in clinical practice.

Main Methods:

  • A deep learning model (DeepLabv3-ResNet-18), named ReticularNet, was trained on 508 NIR images from 117 eyes.
  • The model performed pixel-level segmentation and grading of RPD lesions.
  • Performance was evaluated against manual grading by four ophthalmologists using Dice Similarity Coefficient (DSC) and Intraclass Correlation Coefficient (ICC).

Main Results:

  • ReticularNet achieved a mean DSC of 0.36, significantly outperforming individual ophthalmologists (mean DSCs 0.03-0.23) and the group (P < 0.0001).
  • The model demonstrated high ICC values for lesion number (0.44), pixel area (0.56), and contour area (0.61).
  • ReticularNet's grading showed superior consistency and accuracy compared to human graders.

Conclusions:

  • ReticularNet provides automated, accurate pixel-level grading of RPD on NIR images.
  • This deep learning approach surpasses human grading performance, offering enhanced quantitative and spatial analysis.
  • The availability of this model and code promises to advance RPD research and understanding of AMD biomarkers.