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Related Experiment Video

Updated: Sep 19, 2025

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Identifying Retinal Features Using a Self-Configuring CNN for Clinical Intervention.

Daniel S Kermany^1,2,3,4, Wesley Poon^1,2,4, Anaya Bawiskar^1,3

¹Translational Biophotonics Laboratory, Department of Systems Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas, United States.

Investigative Ophthalmology & Visual Science

|June 17, 2025

Summary

This summary is machine-generated.

We introduce OCTAVE, a large dataset for retinal imaging, to advance AI for diagnosing eye diseases. This resource enables better AI models for precise disease detection and treatment guidance.

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Retinal diseases are a major cause of blindness globally.
Optical coherence tomography (OCT) is crucial for diagnosing retinal conditions.
Limited annotated OCT datasets hinder AI development for retinal disease diagnosis.

Purpose of the Study:

To address the scarcity of annotated 3D OCT datasets.
To introduce OCTAVE, a comprehensive 3D OCT dataset with pixel-level annotations.
To provide annotated public OCT datasets for external validation.

Main Methods:

Developed the OCTAVE dataset with 198 training and 221 validation volumes.
Annotated anatomic and pathological structures at the pixel level.
Trained a deep learning segmentation model (nnU-Net) and evaluated on external datasets.

Main Results:

The OCTAVE dataset contains 198 OCT volumes (3762 B-scans) for training.
221 OCT volumes (4109 B-scans) were used for external validation.
The deep learning model achieved clinically significant performance across all retinal structures.

Conclusions:

Demonstrated robust segmentation performance and generalizability of AI models.
OCTAVE dataset supports AI tool development for precise retinal disease detection and monitoring.
This resource can improve clinical outcomes and advance AI-driven retinal disease management.