LensID — lens & pupil segmentation

Segmentation subsets of LensID (Ghamsarian et al., MICCAI 2021), a cataract-surgery dataset from ITEC, Alpen-Adria-Universität Klagenfurt and the Department of Ophthalmology, Klinikum Klagenfurt. Frames are extracted from surgical microscope video of the anterior segment of the eye.

Modality	Cataract surgery microscope video (RGB), annotated on extracted 2D frames
Anatomy	Eye, anterior segment
Targets	lens (intraocular lens implant), pupil
Images	401 unique (train 292 / test 109)
lens_mask	401 (every row)
pupil_mask	189 (train 141 / test 48) — null on the other 212
Resolutions	1024×768 (299 frames) and 720×720 (102 frames)
Mask format	binary PNG, single channel, values {0, 255}
Official split	preserved as released; no video appears in both splits

Important: lens ⊂ pupil — the two targets NEST

The intraocular lens sits inside the pupil aperture. Measured over all 189 frames that carry both masks:

• 99.59 % of lens pixels fall inside pupil (per-frame minimum 94.01 %)
• only 68.19 % of pupil pixels fall inside lens

They are therefore published as two independent binary masks. Do not merge them into a single {0, 1=pupil, 2=lens} label map — that would silently reduce "pupil" to a rim annulus and change what the benchmark measures.

Important: pupil is an annotation layer, not extra images

The 189 images in the upstream Dataset_pupil.zip are byte-identical duplicates of 189 images in Dataset_lens.zip (md5 match on all 189, same split, same filename). This mirror stores each image once and marks pupil availability with has_pupil_mask. Unique images = 401, not 590.

Pupil annotations cover exactly the 189 non-case_ frames; the 212 case_3xxx frames have a lens mask only.

Naming and grouping

group_id is the safe key for group-wise splitting or video assembly — it is never null. video_id is null only for the 10 t1xxxx frames, whose video of origin is not recoverable from the release.

source_group	example	video_id	n	size
case_XXXX	case_3155_000002	case_3155	212	1024×768
Vnn_nnn	V11_123	V11	87	1024×768
Vn_6digit	V1000006, V000139	V1, V	92	720×720
t1xxxx	t10001	null	10	720×720

The V-series rule is strip the trailing 6 digits. Ten files carry no video digit and share the bare prefix V; grouping them as one video is what makes the paper's counts reconcile exactly — 21 train / 6 test videos for lens, 13 / 3 for pupil, 27 videos in total.

Deviations from the upstream archives

1. Dataset_phase.zip is not mirrored. It is 367 GB of .avi clips for binary Implantation-vs-Rest classification and contains no segmentation masks.
2. One orphan mask dropped: lens/test/V000268_31.png had no matching image (402 masks vs 401 images) and was 512×512 RGB where its group is 720×720 L. A proper V000268 image+mask pair is present and unaffected.
3. Masks normalised to single-channel L. Upstream ships a mix of RGB and L. Every RGB mask was verified to have three identical channels, so this is lossless. Values remain exactly {0, 255}.
4. Images are byte-for-byte the upstream PNGs; no resizing or re-encoding.

Scope note

"LensID" is the name of the framework in the paper. The lens target is the artificial intraocular lens (IOL) implant after implantation — not the natural crystalline lens and not the cataract.

Overlap with other cataract datasets

No frame overlap with Cataract-101 (case_269–case_934 vs LensID's case_3091–case_3262; the ID spaces are disjoint), Cataract-21, IrisPupilSeg, InSegCat or CatRelDet. CaDIS / CATARACTS-2018 were recorded at Brest University Hospital, France; LensID is Klagenfurt, Austria. Cataract-1K shares the same two anatomy targets but was recorded 2021–2023, after LensID published — target duplication, not data duplication. No cross-reference ID column exists upstream.

License

CC BY-NC 4.0, as stated on the official dataset page.

The authors' page adds a further restriction, reproduced verbatim:

This dataset is exclusively provided for scientific research purposes and as such cannot be used commercially or for any other purpose. If any other purpose is intended, you may directly contact the originators of the datasets.

This mirror exists for non-commercial scientific research only. Source: https://ftp.itec.aau.at/datasets/ovid/LensID/

Citation

@inproceedings{ghamsarian2021lensid,
  title     = {LensID: A CNN-RNN-Based Framework Towards Lens Irregularity
               Detection in Cataract Surgery Videos},
  author    = {Ghamsarian, Negin and Taschwer, Mario and
               Putzgruber-Adamitsch, Doris and Sarny, Stephanie and
               El-Shabrawi, Yosuf and Schoeffmann, Klaus},
  booktitle = {MICCAI 2021},
  series    = {LNCS},
  volume    = {12908},
  pages     = {76--86},
  year      = {2021},
  doi       = {10.1007/978-3-030-87237-3_8}
}