---
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}
}
```