AROI: Annotated Retinal OCT Images Database

1,136 OCT B-scans from 24 AMD patients. Expert annotations for 3 retinal fluid types (IRF, SRF, PED) and 3 retinal layer boundaries for joint layer and fluid segmentation.

At a glance

Field	Value
Short name	aroi
Full name	AROI: Annotated Retinal OCT Images Database
Primary category	oct
Contained modalities	oct
Tasks	segmentation
Samples	1,136
Classes	Not reported (Not reported)
Splits	train, test
Size	0.5 GB
Source-stated terms	Research only — cite required papers (see citation field)
Normalized terms	research-only
Descriptive screening label	Research or challenge restriction recorded; check source
Terms scope	dataset_files
Access friction	anonymous_direct
Route backend	Google Drive
Availability	available (checked 2026-07-21)
Acquisition support	loader_implemented_not_live_tested
Legacy sample-loader status	Standard loader included

Access preflight and acquisition

CLI

# Read-only preflight
eyehub download aroi --data-dir ./data --dry-run --json

# Explicit transfer, only when preflight reports supported behavior
eyehub download aroi --data-dir ./data

Python

from eyedatahub.acquisition import preflight_dataset
from eyedatahub.datasets.registry import REGISTRY

ds = REGISTRY.get_dataset('aroi')
print(preflight_dataset(ds, './data'))  # no transfer

Upstream page: drive.google.com/file

Source-term evidence: drive.google.com/file

Loader example

This entry includes a standard DatasetSample loader.

from pathlib import Path
from eyedatahub.datasets.registry import REGISTRY

data_dir = Path('~/.eyedatahub/data').expanduser()
ds = REGISTRY.get_dataset('aroi')
samples = ds.load(data_dir, split='test')
for s in samples[:5]:
    print(s.sample_id, s.label, s.image_path)

Citation

BibTeX

@misc{aroi,
  title  = { AROI: Annotated Retinal OCT Images Database },
  note   = { M. Melinščak, M. Radmilović, Z. Vatavuk, S. Lončarić, 'Annotated retinal optical coherence tomography images (AROI) database for joint retinal layer and fluid segmentation', Automatika, vol. 62, no. 3, pp. 375–385, Jul. 2021. doi:10.1080/00051144.2021.1973298 | M. Melinščak et al., 'AROI: Annotated Retinal OCT Images database', MIPRO 2021, pp. 400–405 | M. Melinščak, 'Attention-based U-net: Joint segmentation of layers and fluids from retinal OCT images', MIPRO 2023, pp. 391–396 | M. Melinščak, 'Enhancing Interpretability in Retinal OCT Analysis Using Grad-CAM: A Study on the AROI Dataset', MIPRO 2025, pp. 1433–1438 },
  year   = { 2021 },
  url    = { https://drive.google.com/file/d/10Ys4xsw81evjHewZEvqHy4Kri0my8C2S/view },
}

Plain text

M. Melinščak, M. Radmilović, Z. Vatavuk, S. Lončarić, 'Annotated retinal optical coherence tomography images (AROI) database for joint retinal layer and fluid segmentation', Automatika, vol. 62, no. 3, pp. 375–385, Jul. 2021. doi:10.1080/00051144.2021.1973298 | M. Melinščak et al., 'AROI: Annotated Retinal OCT Images database', MIPRO 2021, pp. 400–405 | M. Melinščak, 'Attention-based U-net: Joint segmentation of layers and fluids from retinal OCT images', MIPRO 2023, pp. 391–396 | M. Melinščak, 'Enhancing Interpretability in Retinal OCT Analysis Using Grad-CAM: A Study on the AROI Dataset', MIPRO 2025, pp. 1433–1438.

Source-stated terms

• Raw source string: Research only — cite required papers (see citation field)
• Normalized category: research-only
• Apparent scope: dataset_files
• Descriptive screening label: Research or challenge restriction recorded; check source

⚠️ Source-stated terms, scope, and normalized labels are curation metadata, not legal advice or a permission finding. Review the current official source before transfer or reuse.

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