--- license: cc-by-nc-4.0 task_categories: - image-segmentation tags: - medical - ophthalmology - cataract-surgery - surgical-video - intraocular-lens - pupil size_categories: - n<1K --- # 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: ## Citation ```bibtex @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} } ```