OCT Retinal Layer Segmentation — MS & Healthy Controls (JHU/IACL)

35 Spectralis OCT volumes (1,715 B-scans) with 9 manually delineated retinal layer boundaries. 14 healthy controls, 21 MS subjects.

At a glance

Field	Value
Short name	oct_ms_jhu
Full name	OCT Retinal Layer Segmentation — MS & Healthy Controls (JHU/IACL)
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	oct_ms_jhu
Contained modalities	oct
Tasks	segmentation
Primary reported quantity	1,715 b scans
Classes	9 (ILM, RNFL-OPL, OPL-ONL, ELM, MZ, EZ, OSP, IZ-RPE, RPE)
Splits	all
Size	1.8 GB
Source-stated terms	CC BY-NC-ND
Normalized terms	cc-by-nc-nd
Descriptive screening label	Explicit noncommercial clause recorded; check source
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

Reported quantities

Role	Count	Unit	Scope	Basis	Evidence
Primary	1,715	b_scans	B-scans in 35 OCT volumes	associated_publication	https://doi.org/10.1016/j.dib.2018.11.060
Additional	35	volumes	Spectralis OCT volumes	associated_publication	https://doi.org/10.1016/j.dib.2018.11.060

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.

Access information and download

CLI

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

# Download, only when preflight reports supported behavior
eyehub download oct_ms_jhu --data-dir ./data

Python

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

ds = REGISTRY.get_dataset('oct_ms_jhu')
print(preflight_dataset(ds, './data'))  # no download

Upstream page: iacl.ece.jhu.edu/~aaron

Source-term evidence: iacl.ece.jhu.edu/~aaron

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

Citation

BibTeX

@misc{oct_ms_jhu,
  title  = { OCT Retinal Layer Segmentation — MS & Healthy Controls (JHU/IACL) },
  note   = { Y. He et al., 'Retinal layer parcellation of optical coherence tomography images: Data resource for Multiple Sclerosis and Healthy Controls', Data in Brief 22:601-604, 2019 },
  year   = { 2019 },
  url    = { https://iacl.ece.jhu.edu/~aaron/data/OCT_Manual_Delineations-2018_June_29_b.zip },
}

Plain text

Y. He et al., 'Retinal layer parcellation of optical coherence tomography images: Data resource for Multiple Sclerosis and Healthy Controls', Data in Brief 22:601-604, 2019.

Source-stated terms

• Raw source string: CC BY-NC-ND
• Normalized category: cc-by-nc-nd
• Apparent scope: dataset_files
• Descriptive screening label: Explicit noncommercial clause 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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