MGD-1k Meibomian Gland Dataset
One thousand infrared meibography images with meibomian-gland masks, eyelid masks, and six rounds of expert meiboscore grading.
At a glance
| Field | Value |
|---|---|
| Short name | mgd1k |
| Full name | MGD-1k Meibomian Gland Dataset |
| Primary category | external_eye |
| Contained modalities | external_eye |
| Tasks | segmentation, grading |
| Samples | 1,000 |
| Classes | Not reported (Not reported) |
| Splits | all |
| Size | Not reported |
| Source-stated terms | Unknown; project page states All Rights Reserved |
| Normalized terms | unknown |
| Descriptive screening label | Unknown or unclear; do not assume permission |
| Terms scope | unknown |
| Access friction | anonymous_direct |
| Route backend | GitHub |
| Availability | available (checked 2026-07-21) |
| Acquisition support | loader_implemented_not_live_tested |
| Legacy sample-loader status | Metadata and access only |
Notes
Official project page reports 1,000 infrared meibomian-gland images from 320 patients, 1,000 gland masks, 1,000 eyelid masks, and six meiboscore rounds. License is not explicit; verify source terms before reuse.
Access preflight and acquisition
- CLI
- Python
# Read-only preflight
eyehub download mgd1k --data-dir ./data --dry-run --json
# Explicit transfer, only when preflight reports supported behavior
eyehub download mgd1k --data-dir ./data
from eyedatahub.acquisition import preflight_dataset
from eyedatahub.datasets.registry import REGISTRY
ds = REGISTRY.get_dataset('mgd1k')
print(preflight_dataset(ds, './data')) # no transfer
Upstream page: mgd1k.github.io/index.html
Source-term evidence: mgd1k.github.io/index.html
Loader status
This catalog record provides metadata and access instructions, but it does not yet include a standard DatasetSample loader. Inspect the source file structure or contribute a loader before using it in a training pipeline.
Citation
- BibTeX
- Plain text
@misc{mgd1k,
title = { MGD-1k Meibomian Gland Dataset },
note = { Saha RK, Chowdhury AM, Na KS, Hwang GD, Eom Y, Kim J, Jeon HG, Hwang HS, Chung E. Automated quantification of meibomian gland dropout in infrared meibography using deep learning. The Ocular Surface. 2022;26:283-294 },
year = { 2022 },
url = { https://mgd1k.github.io/index.html },
}
Saha RK, Chowdhury AM, Na KS, Hwang GD, Eom Y, Kim J, Jeon HG, Hwang HS, Chung E. Automated quantification of meibomian gland dropout in infrared meibography using deep learning. The Ocular Surface. 2022;26:283-294.
Source-stated terms
- Raw source string: Unknown; project page states All Rights Reserved
- Normalized category:
unknown - Apparent scope:
unknown - Descriptive screening label: Unknown or unclear; do not assume permission
⚠️ 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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