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
First published	Unknown
Publication date precision	Unknown
Publication date evidence	Unknown
Publication date source field	Unknown
Publication date reviewed	Unknown
Primary category	oct
Resource role	current_dataset
Dataset family	tian_oct
Contained modalities	oct
Tasks	segmentation
Primary reported quantity	10 volumes
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	end_to_end_tested
Legacy sample-loader status	Standard loader included

Reported quantities

Role	Count	Unit	Scope	Basis	Evidence
Primary	10	volumes	Primary quantity reported in the reviewed catalog source	legacy_catalog_field	https://doi.org/10.1371/journal.pone.0133908.s002

Counts retain their source-reported units. Additional rows can describe components, paired items, or derivative copies and are not automatically added to the primary quantity.

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 information and download

CLI

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

# Download, 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 download

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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