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FetReg

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medical
fetoscopy
endoscopy
placenta
vessel-segmentation
fetal-surgery
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metadata 
license: cc-by-nc-sa-4.0
task_categories:
  - image-segmentation
tags:
  - medical
  - fetoscopy
  - endoscopy
  - placenta
  - vessel-segmentation
  - fetal-surgery
  - ttts
pretty_name: FetReg2021
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
      - split: train_unlabeled
        path: data/train_unlabeled-*
      - split: test_unlabeled
        path: data/test_unlabeled-*
dataset_info:
  features:
    - name: image
      dtype: image
    - name: mask
      dtype: image
    - name: image_id
      dtype: string
    - name: file_name
      dtype: string
    - name: video_id
      dtype: string
    - name: center_id
      dtype: string
    - name: center
      dtype: string
    - name: challenge_split
      dtype: string
    - name: task
      dtype: string
    - name: frame_index
      dtype: int32
    - name: clip_id
      dtype: string
    - name: clip_frame_index
      dtype: int32
    - name: sequence_index
      dtype: int32
    - name: num_frames_sequence
      dtype: int32
    - name: width
      dtype: int32
    - name: height
      dtype: int32
    - name: fetoplac_subject_id
      dtype: string
    - name: in_fetoplac_annotated
      dtype: bool
  splits:
    - name: train
      num_bytes: 654341601
      num_examples: 2060
    - name: test
      num_bytes: 242931356
      num_examples: 658
    - name: train_unlabeled
      num_bytes: 1959789850
      num_examples: 7411
    - name: test_unlabeled
      num_bytes: 588615799
      num_examples: 2225
  download_size: 3445042447
  dataset_size: 3445678606

FetReg2021

Placental vessel segmentation in in-vivo fetoscopy — the MICCAI/EndoVis 2021 FetReg challenge dataset. Frames come from fetoscopic laser photocoagulation for Twin-to-Twin Transfusion Syndrome (TTTS), captured during 24 procedures at two fetal-surgery centres, and are cropped square to the fetoscope field of view.

There is only one FetReg edition (2021). "FetReg2022" is a citation-year artifact: the challenge-findings paper appeared as a 2022 preprint about the 2021 challenge, and the data went fully public in June 2022.

Contents

One row per frame. The complete official release is mirrored — both challenge tasks, both splits, all 24 procedures, including the real test ground truth.

split task videos frames masks
train 1 — segmentation 18 2,060 ✅
test 1 — segmentation 6 658 ✅
train_unlabeled 2 — registration 18 7,411 ❌
test_unlabeled 2 — registration 6 2,225 ❌
total 24 12,354 2,718

Both labelled splits carry real ground truth, so no train/test fallback is needed. Task 2 ships no ground truth of any kind — no masks and no homographies — by design; the challenge scored it with a proxy N-frame SSIM metric. Those frames are kept here with mask=None so mosaicking and semi-supervised work remain possible.

Video IDs run Video001–Video025 with Video021 absent (25 − 1 = 24).

Classes

Mutually exclusive. Single-channel PNG, PIL mode L, raw integer labels — no palette, no colour lookup. Verified exhaustively: all 2,718 masks contain only {0,1,2,3}, with no anti-aliasing strays.

id class pixel share frames containing it
0 background 88.18% 2,718 / 2,718
1 placental vessel 9.01% 2,691 / 2,718
2 ablation tool 1.35% 901 / 2,718
3 fetus 1.46% 376 / 2,718

The authors' own visualisation script (mirrored here as upstream_FetReg2021_segmentation_visualisation.py) maps these to black / red / blue / green.

Columns

column notes
image RGB PNG, square, 271–720 px (see caveats)
mask mode-L PNG, values 0–3. None in the two *_unlabeled splits
image_id / file_name upstream stem / filename, unchanged
video_id Video001–Video025. Group on this — one procedure, one patient
center_id / center I/UCLH or II/IGG — 12 procedures each
challenge_split train or test, meaningful for the unlabeled splits too
task segmentation (Task 1) or registration (Task 2)
frame_index original frame number in the source video. Task 1 only; None for clips, whose numbering was reset upstream
clip_id / clip_frame_index e.g. CLIP01 and the index within it. Task 2 only
sequence_index 0-based position within this (video_id, task) sequence, ordered by the parsed integer
num_frames_sequence length of that sequence
width / height this frame's own size — it is not constant per video
fetoplac_subject_id anonNNN when this procedure also appears in MedOtter/FetoPlac, else None
in_fetoplac_annotated True if this exact frame is in FetoPlac's 483-frame GT set. None for clips, where the original frame number is unrecoverable

⚠️ Overlap with MedOtter/FetoPlac

MedOtter/FetoPlac (Bano et al., MICCAI 2020) is a UCLH-only subset of these same procedures, not an independent dataset. Joining on the original video frame number embedded in both naming schemes — FetoPlac anon{NNN}_{FFFFF}.png ↔ FetReg Video{NNN}_frame{FFFFF}.png — gives five of FetoPlac's six subjects contained at 100%, 445 of its 482 unique GT frames (92%), every one of them inside FetReg's TRAIN split:

FetoPlac subject FetReg video containment
anon001 Video001 120 / 120
anon002 Video002 101 / 101
anon003 Video003 39 / 39
anon005 Video007 88 / 88
anon012 Video019 97 / 97
anon010 — not in FetReg

Use fetoplac_subject_id to exclude at the procedure level (the safe granularity) and in_fetoplac_annotated for frame-level precision. Do not evaluate FetoPlac against a model trained on FetReg train, and never split these procedures across train and test. FetoPlac's binary vessel masks are also a different annotation of the same pixels, so agreement between the two is not independent evidence.

No overlap with the other EndoVis-family datasets — Endovis2017/2018 are porcine robotic surgery, CholecSeg8k / m2caiSeg / Endoscapes2023 are laparoscopic cholecystectomy. The only shared lineage is the EndoVis umbrella.

Corrections to the upstream documentation

Every count here was measured from the archive's bytes. Five upstream numbers do not survive that check; the values in this mirror are the measured ones.

  1. Video016 train clip has 593 frames, not the README's 493. 593 is what makes the README's own 7,411 train-clip total add up.
  2. Video025 test Task 1 has 110 labelled frames, not the README's 100. 110 is what makes the README's own 658 test total add up.
  3. Video025 test clip has 292 frames, not the 272 in the README and paper Table 2 — so the test-clip total is 2,225 (not 2,205) and the grand total 9,636 (not 9,616).
  4. Paper Table 2's Center column swaps Video018 and Video019. Figures 4 and 5 both give Video018 = II, Video019 = I, and the FetoPlac overlap proves Video019 is UCLH independently. This mirror uses the figures.
  5. Paper Table 2's per-class Occurrence(frame) column is row-shifted from Video020 downward — its Video025 entry (648/320/83) is in fact the test-set column totals. The measured per-video occurrence ships in class_map.json.

Also: the README labels both Test subsections Train_FetReg2021_Task* (copy-paste); the real directories are Test_.... And the 2021 descriptor's claim of three centres including University Hospital Leuven is stale — the final paper and the released archive both have two.

Caveats

  • Resolution varies per video and within a video. Task 1 sizes span 320–720 px. Video010 is the one sequence that changes mid-video: 17 frames at 622×622 and 83 at 638×638. Any code assuming one size per video_id will break.
  • A video's Task 2 clip is not the same geometry as its Task 1 frames — Video023 is 320 px in Task 1 but 271 in its clip; Video022 400 vs 673; Video012 320 vs 277. Do not reuse a Task 1 size for a clip.
  • Only Video010's Task 1 filenames carry a doubled prefix (Video010_frame0Video010_00000.png); the other five test videos use the clean Video{NNN}_frame{NNNNN}.png form. One regex does not cover both. Task 2 clip indices and zero-padding are likewise inconsistent (CLIP00/01/04/ 09; 4-digit in some train videos, 5-digit in others; train clips start at 1, test clips at 0), so this mirror orders on the parsed integer via sequence_index.
  • Severe class imbalance. Tool and fetus are ~1.4% of pixels each and absent from most frames; Video012 contains no fetus at all. Per-class scores are unstable, and a metric that rewards a correctly-empty class will inflate them.
  • Every image was annotated once, so no inter-rater agreement is computable. The pipeline was tiered — 4 researchers annotated 7 videos, a commercial team with clinical background annotated 17, then 2 researchers verified and 2 fetal medicine specialists signed off — but it converged to this single mask set, which is the gold standard.
  • Known annotation-completeness caveat. The authors of TTTSNet (Płotka et al., Med. Image Anal. 2025) re-annotated FetReg's 18 training procedures, stating that these masks "omit small placental vessel segmentation and include incomplete labels for larger vessels". Their release is vessel-only, by different authors, under CC BY 4.0 — it is not FetReg ground truth, but it is a real caveat for vessel-recall comparisons.
  • Frames are pre-cropped square to the fetoscope field of view (an upstream authorial choice). No field-of-view mask ships with FetReg.

Fidelity

Image and mask bytes are copied verbatim from the UCL deposit — no re-encode, no resize, no relabelling. Only the container changed (per-video directories → parquet) and metadata columns were added.

License

CC BY-NC-SA 4.0, inherited from the source deposit. ShareAlike applies: this reformatted derivative carries the same license. Non-commercial use only.

Source

Note: the URLs cited in the papers and on Synapse (weiss-develop.cs.ucl.ac.uk, the UCL WEISS open-data page, fetreg2021.grand-challenge.org) are all dead or redirected — WEISS was folded into the UCL Hawkes Institute and the data moved to the RDR deposit above.

Citation

Both are requested by the upstream README.

@article{bano2024fetreg,
  title   = {Placental vessel segmentation and registration in fetoscopy:
             Literature review and MICCAI FetReg2021 challenge findings},
  author  = {Bano, Sophia and Casella, Alessandro and Vasconcelos, Francisco and
             Qayyum, Abdul and Benzinou, Abdesslam and Mazher, Moona and
             Meriaudeau, Fabrice and others and Moccia, Sara and Stoyanov, Danail},
  journal = {Medical Image Analysis},
  volume  = {92},
  pages   = {103066},
  year    = {2024},
  doi     = {10.1016/j.media.2023.103066}
}

@article{bano2021fetreg,
  title   = {FetReg: Placental Vessel Segmentation and Registration in
             Fetoscopy Challenge Dataset},
  author  = {Bano, Sophia and Casella, Alessandro and Vasconcelos, Francisco and
             Moccia, Sara and Attilakos, George and Wimalasundera, Ruwan and
             David, Anna L and Paladini, Dario and Deprest, Jan and
             De Momi, Elena and Mattos, Leonardo S and Stoyanov, Danail},
  journal = {arXiv preprint arXiv:2106.05923},
  year    = {2021}
}