| --- |
| license: cc-by-nc-sa-4.0 |
| task_categories: |
| - image-segmentation |
| tags: |
| - medical |
| - fetoscopy |
| - placenta |
| - vessel |
| - segmentation |
| - endoscopy |
| - video |
| size_categories: |
| - n<10K |
| --- |
| |
| # FetoPlac — Fetoscopic Placental Vessel Segmentation (Bano et al., 2020) |
|
|
| Re-hosted mirror of the **Fetoscopy Placenta Dataset** released alongside |
| *"Deep Placental Vessel Segmentation for Fetoscopic Mosaicking"* |
| (Bano et al., MICCAI 2020). Intended for use with the |
| [EasyMedSeg](https://github.com/) benchmark. |
|
|
| ## Why this mirror exists |
|
|
| The canonical UCL WEISS host |
| (`weiss-develop.cs.ucl.ac.uk/fetoscopy-data/...`) went offline when the |
| WEISS lab merged into the Hawkes Institute and the open-data subtree was |
| retired. The full archive is **not** available on Zenodo, OSF, or |
| Kaggle, and the broader FetReg2021 Synapse release is a different |
| (gated, multi-class) dataset. This HF mirror is built from the |
| byte-identical Internet Archive Wayback snapshot |
| (`web.archive.org/web/20220412011703id_/...`, ZIP magic + size verified |
| against the original Apache `Content-Length` header). |
|
|
| ## Composition |
|
|
| Two splits with **unified schema**: |
|
|
| | Split | Frames | Source | Mask type | |
| |--------------|-------:|-------------------|----------------------| |
| | `test` | 483 | 6 surgeries | manual binary GT | |
| | `unannotated` | 950 | 6 continuous clips | U-Net pseudo-labels | |
| | **All** | **1433** | | | |
|
|
| Per-video frame counts: |
|
|
| | Video | `test` (GT) | `unannotated` | Native resolution | |
| |----------|------------:|--------------:|-------------------| |
| | video01 | 121 | 400 | 470 x 470 (test); 448 x 448 (ua) | |
| | video02 | 101 | 200 | 540 x 540 / 448 | |
| | video03 | 39 | 50 | 550 x 550 / 448 | |
| | video04 | 88 | 100 | 640 x 640 / 448 | |
| | video05 | 37 | 100 | 640 x 640 / 448 | |
| | video06 | 97 | 100 | 720 x 720 / 448 | |
|
|
| The paper protocol is **6-fold leave-one-video-out cross-validation** on |
| the 483 GT frames — `video_id` is exposed in every row to make this |
| reproducible. There is no fixed train/val/test partition in the |
| original release; we expose all 483 GT frames under the `test` split |
| because EasyMedSeg evaluation reads from `test` by default. |
|
|
| ## Schema (both splits) |
|
|
| | Column | Type | Description | |
| |-------------|----------|------------------------------------------------------------| |
| | `image_id` | `string` | Frame stem (e.g. `anon001_02785`) | |
| | `image` | `Image` | Source RGB frame (PNG bytes, native size) | |
| | `mask` | `Image` | Vessel mask (PNG bytes). GT for `test`, pseudo for `unannotated` | |
| | `fov_mask` | `Image` | Per-video circular field-of-view ROI mask (1-bit PNG) | |
| | `video_id` | `string` | `video01` .. `video06` | |
| | `mask_type` | `string` | `"gt"` for `test`, `"predicted"` for `unannotated` | |
|
|
| ### Mask decoding |
|
|
| The upstream PNG masks use **non-canonical pixel encodings**: per-video |
| the dominant non-background value is **25 or 33** (an artifact of the |
| PixelAnnotationTool palette index), plus minor anti-aliasing values |
| (1, 2, 3, 15) at <0.5% of pixels. The canonical decoding rule is: |
|
|
| ```python |
| binary_vessel = (np.array(mask)[..., 0] > 0).astype(np.uint8) |
| ``` |
|
|
| Pseudo-labels in the `unannotated` split are continuous probability |
| maps with ~256 unique grayscale values; threshold at the value |
| required by downstream usage. |
|
|
| ### FoV mask |
|
|
| The 6 per-video FoV masks are circular endoscope field-of-view stencils |
| (boolean), embedded per-row for convenience. Apply via: |
|
|
| ```python |
| fov = np.array(row["fov_mask"]).astype(bool) |
| masked_image = np.array(row["image"]) * fov[..., None] |
| ``` |
|
|
| ## License |
|
|
| **CC BY-NC-SA 4.0** (Attribution-NonCommercial-ShareAlike 4.0 |
| International), as stated in the original `Read me.txt` shipped with |
| the zip. Research / non-commercial use only. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{bano2020deep, |
| title = {Deep Placental Vessel Segmentation for Fetoscopic Mosaicking}, |
| author = {Bano, Sophia and Vasconcelos, Francisco and Shepherd, Luke M. and |
| Vander Poorten, Emmanuel and Vercauteren, Tom and Ourselin, Sebastien and |
| David, Anna L. and Deprest, Jan and Stoyanov, Danail}, |
| booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2020}, |
| series = {Lecture Notes in Computer Science}, |
| volume = {12263}, |
| pages = {763--773}, |
| publisher = {Springer}, |
| year = {2020}, |
| doi = {10.1007/978-3-030-59716-0_73} |
| } |
| ``` |
|
|
