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

Analysis of human visual experience data.

Journal of vision·2026
Same author

Repeatability of SpotChecks contrast sensitivity test in macular disease.

Optometry and vision science : official publication of the American Academy of Optometry·2026
Same author

Time of Day Effects of Contact Lens-Induced Full-Field and Peripheral-Field Defocus on Choroidal Thickness: A Pilot Study in Adults.

Eye & contact lens·2026
Same author

Retinal Safety of a Red Light Myopia Therapy Device-Reply.

JAMA ophthalmology·2026
Same author

Dose-Response Characteristics of Retinal Inactivation Due to Intravitreal Injections of Tetrodotoxin in Nonhuman Primates.

Investigative ophthalmology & visual science·2026
Same author

Relationship between form-deprivation myopia and amblyopic deficit.

Vision research·2026

Related Experiment Video

Updated: May 17, 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

2.6K

Deep learning-based segmentation of OCT images for choroidal thickness.

Raman Prasad Sah1, Nimesh B Patel1, Hope M Queener1

  • 1University of Houston College of Optometry, 4401 MLK Blvd, Houston, TX 77204, USA.

Journal of Optometry
|May 6, 2025
PubMed
Summary

A new deep learning algorithm accurately segments choroidal thickness from optical coherence tomography (OCT) scans. This automated method shows excellent agreement with manual segmentation, offering a more objective and efficient approach.

Keywords:
Automated segmentationChoroidal thicknessDeep learningNeural networkOptical coherence tomography

More Related Videos

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
08:50

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Published on: February 9, 2019

7.6K
Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo
12:54

Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo

Published on: October 2, 2021

3.2K

Related Experiment Videos

Last Updated: May 17, 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

2.6K
Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
08:50

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Published on: February 9, 2019

7.6K
Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo
12:54

Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo

Published on: October 2, 2021

3.2K

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate measurement of choroidal thickness is crucial for diagnosing and monitoring various eye conditions.
  • Manual segmentation of optical coherence tomography (OCT) scans is time-consuming and subjective.

Purpose of the Study:

  • To develop and validate a custom deep learning-based automated segmentation tool for choroidal thickness in OCT scans.
  • To compare the performance of the in-house automated method against manual segmentation and an open-source algorithm.

Main Methods:

  • A Deeplabv3+ network (ResNet50) was trained on 10,798 manually segmented OCT scans.
  • Validation involved comparing manual and in-house automated segmentation on 130 unique scans using Bland-Altman analysis, ICC, and Deming regression.
  • The in-house method was also benchmarked against an open-source algorithm.

Main Results:

  • The in-house automated method showed no significant difference in mean choroidal thickness compared to manual segmentation across different regions (P > 0.05).
  • Excellent agreement was observed between manual and in-house automated methods (ICC: 0.96-0.98, P < 0.001).
  • An open-source algorithm yielded consistently thinner choroidal thickness measurements than both manual and in-house automated methods.

Conclusions:

  • Custom deep learning automated choroid segmentation demonstrates excellent agreement with manual segmentation.
  • The automated approach provides objective and efficient estimation of choroidal thickness.
  • This technology has the potential to improve clinical workflows and diagnostic accuracy in ophthalmology.