AROMA dataset (Artifact Recognition on OCT-A, Modeling, and Analysis)

General description

One of the purpose of the AROMA study was to release a publicly available database, consisting of en face retinal SS OCT-A images labeled by artifact type and severity.

This dataset includes:

• OCT-A scans (n = 281), each decomposed into 14 .png En Face images (C-scans);
• an Excel file that indicates the intensity of artifacts present and the Signal Strength for each OCT-A scan. The following artifacts were rated from 0 to 3: “Motion/Blink”, “Tilt”, “Focus”, “Decentration/Z-offset”, “Shadow/Masking”, “FAZ segmentation”, and “Refractive Shift”;
• a python script to read and preview data of a random patient.

Data origin

This dataset was acquired from the Department of Ophthalmology at the University Hospital of Dijon, France, and consists of clinical acquisitions from various registered clinical studies. It was anonymized and processed in accordance with the rules established by the Ethics Committee of the University Hospital of Dijon. All administrative information contained in the metadata was removed, making the dataset untraceable and fully anonymized. Selected patients did not have any macular disease or severe ocular media alteration. Only one eye per patient was analyzed.

OCT-A scans

All OCT-A scans were performed using the same PLEX Elite 9000® device (Zeiss, Carl Zeiss Meditec Inc., Dublin, OH, USA) after instillation of one drop of tropicamide 0.5%. One OCT-A scan was collected per visit, with a maximum of two visits per participant. OCT-A image sizes were 3 × 3 mm and/or 6 × 6 mm. If both scan sizes were available for a participant, each scan was considered a separate instance in the analysis. All OCT-A scans were acquired with a frequency of 100 Hz. Only scans with a signal strength ≥ 7 were included. The PLEX Elite 9000® acquisition sequence includes in-built artifact correction algorithms, notably for motion and projection.

En face and layered image acquisitions

After acquisition, each OCT-A scan was exported to the proprietary cloud platform ARI-Network® (Carl Zeiss Meditec Inc., Dublin, OH, USA), where it was processed using a dedicated algorithm (Macular Density v0.7.3). The algorithm computed each OCT-A scan into 14 different images according to the layer studied and the type of image processing performed. Depending on the initial acquisition size (3 × 3 mm or 6 &ti