Tian OCTRIMA 3D OCT Layer Segmentation Dataset

10 Heidelberg Spectralis SD-OCT volumes (510 B-scans, 496×644×51 voxels) from healthy subjects with 8 retinal layer boundary annotations by 2 independent observers. MATLAB .mat format; used as the OCTRIMA 3D validation set.

At a glance

Field	Value
Short name	tian_oct
Full name	Tian OCTRIMA 3D OCT Layer Segmentation Dataset
Primary category	oct
Contained modalities	oct
Tasks	segmentation
Samples	10
Classes	8 (ILM, RNFL_GCL_IPL, IPL_INL, INL_OPL, OPL_ONL, ELM, IS_OS, OS_RPE)
Splits	test
Size	0.2 GB
Source-stated terms	CC BY 4.0
Normalized terms	cc-by
Descriptive screening label	Standard label without an explicit NC clause; not a permission finding
Terms scope	dataset_files
Access friction	anonymous_direct
Route backend	Direct HTTP
Availability	available (checked 2026-07-21)
Acquisition support	loader_implemented_not_live_tested
Legacy sample-loader status	Standard loader included

Notes

Primarily a benchmark/test set (10 volumes, 2 observers). Each .mat file contains: raw OCT volume, OCTRIMA 3D automatic segmentation, Observer 1 manual, Observer 2 manual. Supplement URL: https://doi.org/10.1371/journal.pone.0133908.s002

Access preflight and acquisition

CLI

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

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

Python

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

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

Upstream page: https://doi.org/10.1371/journal.pone.0133908.s002

Source-term evidence: https://doi.org/10.1371/journal.pone.0133908.s002

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('tian_oct')
samples = ds.load(data_dir, split='test')
for s in samples[:5]:
    print(s.sample_id, s.label, s.image_path)

Citation

BibTeX

@misc{tian_oct,
  title  = { Tian OCTRIMA 3D OCT Layer Segmentation Dataset },
  note   = { Tian J et al., 'Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region', PLOS ONE 10(8):e0133908 (2015). doi:10.1371/journal.pone.0133908 },
  year   = { 2015 },
  url    = { https://doi.org/10.1371/journal.pone.0133908.s002 },
}

Plain text

Tian J et al., 'Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region', PLOS ONE 10(8):e0133908 (2015). doi:10.1371/journal.pone.0133908

Source-stated terms

• Raw source string: CC BY 4.0
• Normalized category: cc-by
• Apparent scope: dataset_files
• Descriptive screening label: Standard label without an explicit NC clause; not a permission finding

⚠️ 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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