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README.md

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 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: lens_mask
-    dtype: image
-  - name: pupil_mask
-    dtype: image
-  - name: image_id
-    dtype: string
-  - name: video_id
-    dtype: string
-  - name: group_id
-    dtype: string
-  - name: frame_index
-    dtype: int32
-  - name: source_group
-    dtype: string
-  - name: split
-    dtype: string
-  - name: height
-    dtype: int32
-  - name: width
-    dtype: int32
-  - name: has_pupil_mask
-    dtype: bool
-  splits:
-  - name: train
-    num_bytes: 303231129
-    num_examples: 292
-  - name: test
-    num_bytes: 110730850
-    num_examples: 109
-  download_size: 414069282
-  dataset_size: 413961979
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+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:
+<https://ftp.itec.aau.at/datasets/ovid/LensID/>
+
+## 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}
+}
+```