CAVRI: Computer Analysis of VitreoRetinal Interface Dataset
50 annotated 3D SD-OCT volumes (7,050 B-scans, 640×385 px) from subjects with vitreomacular adhesion (VMA, 25 eyes) and vitreomacular traction (VMT, 25 eyes). Each volume: 141 B-scans over 2×7×7 mm. Annotated boundaries: PCV, ILM, RPE. Device: Optovue Avanti RTvue.
At a glance
| Field | Value |
|---|---|
| Short name | cavri |
| Full name | CAVRI: Computer Analysis of VitreoRetinal Interface Dataset |
| Primary category | oct |
| Contained modalities | oct |
| Tasks | segmentation |
| Samples | 50 |
| Classes | 3 (PCV, ILM, RPE) |
| Splits | train, val, test |
| Size | 3.0 GB |
| Source-stated terms | Research/educational use only (no commercial redistribution) |
| Normalized terms | research-only |
| Descriptive screening label | Research or challenge restriction recorded; check source |
| Terms scope | unknown |
| Access friction | author_contact |
| Route backend | Manual (upstream-gated) |
| Availability | available (checked 2026-07-21) |
| Acquisition support | manual_access_blocked |
| Legacy sample-loader status | Standard loader included |
Notes
Access by email request: [email protected] or [email protected]. Dataset page: https://dsp.put.poznan.pl/cavri_database-191/ GitHub (segmentation code): https://github.com/krzyk87/pcv_segmentation
Access preflight and acquisition
- CLI
- Python
# This route requires upstream human action; no transfer starts.
eyehub download cavri --data-dir ./data --dry-run --json
# Follow the official instructions shown by preflight.
from eyedatahub.acquisition import preflight_dataset
from eyedatahub.datasets.registry import REGISTRY
ds = REGISTRY.get_dataset('cavri')
print(preflight_dataset(ds, './data')) # returns manual_access_blocked
Upstream page: dsp.put.poznan.pl/cavri_database-191
Source-term evidence: dsp.put.poznan.pl/cavri_database-191
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('cavri')
samples = ds.load(data_dir, split='test')
for s in samples[:5]:
print(s.sample_id, s.label, s.image_path)
Citation
- BibTeX
- Plain text
@misc{cavri,
title = { CAVRI: Computer Analysis of VitreoRetinal Interface Dataset },
note = { Stankiewicz A et al., 'Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks', Sensors 21(22):7521 (2021). doi:10.3390/s21227521 },
year = { 2021 },
url = { https://dsp.put.poznan.pl/cavri_database-191/ },
}
Stankiewicz A et al., 'Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks', Sensors 21(22):7521 (2021). doi:10.3390/s21227521
Source-stated terms
- Raw source string: Research/educational use only (no commercial redistribution)
- Normalized category:
research-only - Apparent scope:
unknown - 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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