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) |
| Primary category | oct |
| Contained modalities | oct |
| Tasks | segmentation |
| Samples | 1,715 |
| 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 |
Access preflight and acquisition
- CLI
- Python
# Read-only preflight
eyehub download oct_ms_jhu --data-dir ./data --dry-run --json
# Explicit transfer, only when preflight reports supported behavior
eyehub download oct_ms_jhu --data-dir ./data
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 transfer
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
- Plain text
@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 },
}
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.
Related datasets with shared modalities
- syn_oct: SYN-OCT Synthetic Glaucoma OCT Dataset (200,000 records,
cc-by) - eyecare_100k: Eyecare-100K: Multimodal Ophthalmology VQA Corpus (102,000 records,
unknown) - kermany_oct: Kermany OCT 2018: Retinal OCT Image Classification (84,484 records,
cc-by) - multieye: MultiEYE: OCT-Enhanced Fundus Multi-Disease Benchmark (58,036 records,
mit) - lmod_plus: LMOD+ Multimodal Ophthalmology Benchmark (32,633 records,
unknown) - harvard_fairvision: Harvard-FairVision (AMD + DR + Glaucoma, paired SLO + OCT) (30,000 records,
cc-by-nc-nd) - mario: MARIO: AMD-Progression Longitudinal OCT (MICCAI 2024) (30,000 records,
cc-by) - mmrdr: MMRDR: Multi-Modal Retinal Diabetic Retinopathy Dataset (24,460 records,
cc-by)