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 Experiment Video

Updated: May 31, 2026

In Vivo Vascular Injury Readouts in Mouse Retina to Promote Reproducibility
07:35

In Vivo Vascular Injury Readouts in Mouse Retina to Promote Reproducibility

Published on: April 21, 2022

Retinal lesion annotation on fundus imaging: an interobserver variability study.

Gabriel Lepetit-Aimon1, Clément Playout2,3, Marie Carole Boucher2,3

  • 1Polytechnique Montréal, QC H3T 1J4, Montréal, Canada. gabriel.lepetit-aimon@polymtl.ca.

Scientific Reports
|May 28, 2026
PubMed
Summary

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

Investigating Physical Exercise and Cognitive Training Effects on Cognition and Brain Health in Men and Women with Heart Failure: The ReCARDIO Trial.

CJC open·2026
Same author

Multimodal Deep Learning for Benign vs Malignant Eyelid Lesion Classification Using Optical Coherence Tomography and Clinical Images.

Ophthalmic plastic and reconstructive surgery·2026
Same author

Mapping neuro-vascular unit communications reveals distinct angiogenic programs across developing mouse brain regions.

Nature communications·2026
Same author

Impact of AI-enhanced three-dimensional OCT scans on disease activity assessment in patients with nAMD: the RAZORBILL study.

The British journal of ophthalmology·2026
Same author

Automatic dental crown generation with spatial constraint modeling.

Journal of medical imaging (Bellingham, Wash.)·2026
Same author

DNAi: an open-source AI tool for unbiased DNA fiber analysis.

Nucleic acids research·2026
Same journal

Clinical crown height changes in mandibular anterior teeth retained with two types of fixed retainers over two years: findings from a randomized clinical trial.

Scientific reports·2026
Same journal

Rethinking water governance through indigenous systems: A comparative assessment of qanat and well irrigation productivity in Sabzevar County, Iran.

Scientific reports·2026
Same journal

Distributed Nash equilibrium seeking for second-order systems with finite/fixed-time convergence in the absence of velocity measurement.

Scientific reports·2026
Same journal

Determinants of pregnancy termination among ever-married women of reproductive age in Bangladesh.

Scientific reports·2026
Same journal

Occurrence and human health risk assessment of organochlorine pesticides in irrigated and non-irrigated agricultural soils of Wondogenet District, Ethiopia.

Scientific reports·2026
Same journal

High angular resolution diffusion imaging of neurodevelopment in children through data creation with deep learning.

Scientific reports·2026
See all related articles
This summary is machine-generated.

Manual annotation of diabetic retinopathy lesions shows high variability among experts. A semi-automated workflow significantly improved agreement and accuracy in lesion segmentation for better DR diagnosis.

Area of Science:

  • Ophthalmology and Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Accurate segmentation of retinal lesions in fundus images is crucial for diabetic retinopathy (DR) diagnosis.
  • The reliability of manual annotation by clinical experts, essential for training machine learning models, has not been thoroughly evaluated.

Purpose of the Study:

  • To quantify interobserver variability in manual pixel-wise annotation of key retinal lesions (microaneurysms, hemorrhages, exudates, cotton-wool spots).
  • To evaluate the effectiveness of a semi-automated workflow in improving the consistency and accuracy of retinal lesion segmentation.

Main Methods:

  • Three senior retinologists independently annotated 51 fundus images from the MAPLES-DR dataset for microaneurysms, hemorrhages, hard exudates, and cotton-wool spots.
  • Interobserver variability was assessed using metrics such as lesion detection rates, coordinate differences, and mean Intersection over Union (IoU).
Keywords:
Diabetic retinopathyFundus photographyInterobserver variabilityRetinal lesion segmentationSemi-automated annotation

More Related Videos

Quantitative Fundus Autofluorescence for the Evaluation of Retinal Diseases
07:22

Quantitative Fundus Autofluorescence for the Evaluation of Retinal Diseases

Published on: March 11, 2016

Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Related Experiment Videos

Last Updated: May 31, 2026

In Vivo Vascular Injury Readouts in Mouse Retina to Promote Reproducibility
07:35

In Vivo Vascular Injury Readouts in Mouse Retina to Promote Reproducibility

Published on: April 21, 2022

Quantitative Fundus Autofluorescence for the Evaluation of Retinal Diseases
07:22

Quantitative Fundus Autofluorescence for the Evaluation of Retinal Diseases

Published on: March 11, 2016

Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

  • A semi-automated workflow involving correction of model-generated pre-segmentations was evaluated.

    • Main Results:

    • Manual segmentation exhibited substantial variability, with 38% of lesions missed by other observers and a mean interobserver IoU of 0.47.
    • Despite high variability in lesion annotation, inter-expert DR grading assessments were largely concordant.
    • The semi-automated workflow significantly improved agreement, achieving a mean IoU of 0.71 and preserving over 90% of lesions from pre-segmentations.

    Conclusions:

    • Manual annotation of retinal lesions by experts is inconsistent, highlighting challenges in developing reliable AI models based solely on manual segmentation.
    • A semi-automated approach, where experts refine AI-generated segmentations, substantially enhances interobserver agreement and accuracy for retinal lesion detection.
    • Semi-automated workflows show promise for improving the efficiency and reliability of data annotation in diabetic retinopathy research.