Cataract-1K: Large-Scale Cataract Surgery Video Dataset

1000 cataract surgery videos from multiple surgeons annotated for 10 surgical phases, instrument segmentation, and tool presence detection. First large-scale cataract surgical video dataset.

At a glance

Field	Value
Short name	cataract1k
Full name	Cataract-1K: Large-Scale Cataract Surgery Video Dataset
Primary category	surgical_video
Contained modalities	surgical_video
Tasks	phase_recognition, segmentation, detection
Samples	1,000
Classes	10 (Incision, Viscoelastic, Capsulorhexis, Hydrodissection, Phacoemulsification, Irrigation-Aspiration, Capsule Polishing, Lens Implant, Viscoelastic Suction, Tonifying-Antibiotics)
Splits	train, val, test
Size	50.0 GB
Source-stated terms	Research only — see Synapse terms
Normalized terms	research-only
Descriptive screening label	Research or challenge restriction recorded; check source
Terms scope	dataset_files
Access friction	controlled_or_manual
Route backend	Manual (upstream-gated)
Availability	available (checked 2026-07-21)
Acquisition support	manual_access_blocked
Legacy sample-loader status	Standard loader included

Notes

Data hosted on Synapse (free registration required). Install synapseclient: pip install synapseclient. Then: synapse get syn53404917

Access preflight and acquisition

CLI

# This route requires upstream human action; no transfer starts.
eyehub download cataract1k --data-dir ./data --dry-run --json
# Follow the official instructions shown by preflight.

Python

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

ds = REGISTRY.get_dataset('cataract1k')
print(preflight_dataset(ds, './data'))  # returns manual_access_blocked

Upstream page: synapse.org

Source-term evidence: synapse.org

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('cataract1k')
samples = ds.load(data_dir, split='test')
for s in samples[:5]:
    print(s.sample_id, s.label, s.image_path)

Citation

BibTeX

@misc{cataract1k,
  title  = { Cataract-1K: Large-Scale Cataract Surgery Video Dataset },
  note   = { Ghamsarian N et al., 'Cataract-1K: Cataract Surgery Dataset for Domain Generalization of Surgical Workflow Analysis', IEEE TMI 2024. https://github.com/Negin-Ghamsarian/Cataract-1K },
  year   = { 2024 },
  url    = { https://www.synapse.org/#!Synapse:syn53404917 },
}

Plain text

Ghamsarian N et al., 'Cataract-1K: Cataract Surgery Dataset for Domain Generalization of Surgical Workflow Analysis', IEEE TMI 2024. https://github.com/Negin-Ghamsarian/Cataract-1K

Source-stated terms

• Raw source string: Research only — see Synapse terms
• Normalized category: research-only
• Apparent scope: dataset_files
• Descriptive screening label: Research or challenge restriction 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

• ophora: Ophora-160K: Ophthalmic Surgical Video Instruction Dataset (160,185 records, unknown)
• lmod_plus: LMOD+ Multimodal Ophthalmology Benchmark (32,633 records, unknown)
• cadis: CaDIS: Cataract Dataset for Image Segmentation (4,670 records, research-only)
• cataract_lmm: Cataract-LMM: Large-Scale Multi-Source Multi-Task Cataract Surgery Benchmark (3,000 records, cc-by-nc-nd)
• ophnet2024: OphNet2024: Ophthalmic Surgical Video Dataset (2,278 records, cc-by-nc-sa)
• migs_video: Multicenter Fine-Annotated MIGS Surgical Video Dataset (186 records, cc-by)
• sics155: SICS-155 Small Incision Cataract Surgery Videos (155 records, cc-by-nc)
• cataract101_extended_labels: Cataract-101 Extended Labels (101 records, cc-by)