OCTAVE Dataset: Optical Coherence Tomography Annotated Volume Experiment

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 Instructions for using this dataset or replicating the results of the paper can be found on the OCTAVE GitHub page 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 availableAcknowledgments 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.).