Published June 2, 2025
| Version 1
Dataset
Open
OCTAVE Dataset: Optical Coherence Tomography Annotated Volume Experiment
Authors/Creators
-
1.
Houston Methodist
-
2.
Texas A&M University
Description
This is a large dataset of OCT 3D image volumes and pixel-level segmentation labels for the development of an deep learning model to autonomously detect and identify anatomic and pathological features of the retina.
This project was described in the paper:
“Identifying Retinal Features Using a Self‑Configuring CNN for Clinical Intervention”
Daniel S. Kermany, Wesley Poon, Anaya Bawiskar, Natasha Nehra, Orhun Davarci, Glori Das, Matthew Vasquez, Shlomit Schaal, Raksha Raghunathan & Stephen T. C. Wong Invest. Ophthalmol. Vis. Sci., June 2, 2025; PMID 40525921
This dataset is made available for use in research only. Use of this dataset requires appropriate citation of both this dataset (DOI: 10.5281/zenodo.14580071) and the associated paper (DOI: 10.1167/iovs.66.6.55).
Important: Ensure you are using the latest version of this dataset, if multiple versions available
Acknowledgments
Acknowledgments
Supported by the National Eye Institute F31EY037177 (D.S.K.)
National Cancer Institute R01CA288613 (S.T.C.W.)
National Cancer Institute R01NS140292 (S.T.C.W.)
T.T. and W.F. Chao Foundation (S.T.C.W.)
John S. Dunn Research Foundation (S.T.C.W.)
Johnsson Estate (S.T.C.W.).
Files
OCTAVE.zip
Additional details
Related works
- Is supplement to
- Journal article: 10.1167/iovs.66.6.55 (DOI)
Funding
- National Eye Institute
- Optical Coherence Tomography (OCT)-Guided Ultrafast, Nonthermal Laser Microablation for Non-invasive Vitreoretinal Surgery F31EY037177
- National Cancer Institute
- Advancing Thyroid Cancer Diagnostics with AI-enhanced Multimodal Optical Histopathology R01CA288613
- National Cancer Institute
- DeepStroke+: An Advanced Mobile AI Diagnostic Tool for Fast and Precise Detection of Acute Strokes in Mobile Stroke Units, Emergency Rooms, and Telestroke Triage R01NS140292
- John S. Dunn Foundation
Software
- Repository URL
- https://github.com/Translational-Biophotonics-Laboratory/octvision3d
- Programming language
- Python
- Development Status
- Active
References
- Kafieh R, Rabbani H, Abramoff MD, Sonka M. Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map. Med Image Anal. 2013; 17(8): 907–928.
- Rasti R, Rabbani H, Mehridehnavi A, Hajizadeh F. Macular OCT classification using a multi-scale convolutional neural network ensemble. IEEE Trans Med Imaging. 2018; 37(4): 1024–1034.
- Stankiewicz A, Marciniak T, Dabrowski A, Stopa M, Marciniak E, Obara B. Segmentation of preretinal space in optical coherence tomography images using deep neural networks. Sensors. 2021; 21(22): 7521.
- Tian J, Varga B, Somfai GM, Lee W-H, Smiddy WE, Cabrera DeBuc D. Real-time automatic segmentation of optical coherence tomography volume data of the macular region. PLoS One. 2015; 10(8): e0133908.