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crossmoda2022

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CrossMoDA2022
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metadata 
license: cc-by-nc-sa-4.0
task_categories:
  - image-segmentation
tags:
  - medical
  - MRI
  - segmentation
  - CrossMoDA2022
  - vestibular-schwannoma
size_categories:
  - n<1K
pretty_name: crossMoDA 2022 (labeled source-training arm, ceT1)
dataset_info:
  features:
    - name: patient_id
      dtype: string
    - name: institution
      dtype: string
    - name: mri_sequence
      dtype: string
    - name: in_crossmoda2021
      dtype: bool
    - name: crossmoda2021_id
      dtype: string
    - name: koos
      dtype: int32
    - name: post_operative
      dtype: bool
    - name: num_slices
      dtype: int32
    - name: shape
      dtype: string
    - name: image
      dtype: image
    - name: mask
      dtype: image
    - name: overlay
      dtype: image
  splits:
    - name: train
      num_bytes: 53266199
      num_examples: 210
  download_size: 53263643
  dataset_size: 53266199
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

crossMoDA 2022 Dataset — labeled source-training arm

MRI from the MICCAI 2022 Cross-Modality Domain Adaptation (crossMoDA) challenge: vestibular schwannoma (VS) and cochlea segmentation. This mirror contains the 210 contrast-enhanced T1 (ceT1) scans with ground-truth masks — the challenge's labeled source-domain training arm — from both 2022 institutions:

Institution Cases Filename stems Native geometry
London (SC-GK) 105 crossmoda2021_ldn_{1..105} 512x512x120 @ 0.41x0.41x1.5 mm (Siemens Avanto 1.5T)
Tilburg (ETZ) 105 crossmoda2022_etz_{0..104} 256x256x50 @ 0.82x0.82x1.5 mm (Philips Ingenia 1.5T)

Volumes are in native per-site geometry — not resampled, not co-registered.

Not comparable to the challenge leaderboard (ceT1 != hrT2)

These are source-domain ceT1 scans. The crossMoDA competition evaluates on the hrT2 target domain, whose validation/test ground truth is withheld (server-side scoring on grand-challenge.org). Scores computed on this repo are valid measurements on ceT1 but are not comparable to crossMoDA leaderboard numbers. Each row's mri_sequence column records this in the data itself.

Overlap with crossMoDA 2021 — machine-readable

The London arm is a voxel-identical duplicate of MedOtter/crossmoda2021: all 210 London file pairs (105 ceT1 scans AND 105 label masks) match at decompressed-content level (md5 of the full NIfTI stream, verified 2026-08-07). The 2022 release only renamed crossmoda_N_* to crossmoda2021_ldn_N_*; the outlines were not re-drawn. Every train.jsonl row therefore carries:

  • in_crossmoda2021 (bool) — true for all 105 London cases
  • crossmoda2021_id (string|null) — the exact 2021 stem (crossmoda_N), null for Tilburg
  • institution ("London" / "Tilburg")

Do not evaluate this repo and crossmoda2021 as independent benchmarks. For a leak-free benchmark restricted to genuinely new data, filter institution == "Tilburg".

Lineage: the London cases descend from the public TCIA collection Vestibular-Schwannoma-SEG (CC BY 4.0, DOI 10.7937/TCIA.9YTJ-5Q73); institution == "London" is the collection-level marker for that lineage. crossMoDA 2023 (Synapse syn51236108) is a superset of this material.

Omitted from this mirror (by design)

The official Zenodo release (record 6504722) additionally contains 210 unlabeled hrT2 training-target scans, 64 validation hrT2 scans (GT withheld), and GIF auto-parcellations (algorithm outputs, not ground truth); the 271-scan test set was never publicly released. None of those carry usable public ground truth, so this mirror hosts only the labeled arm. Fetch the omitted parts from Zenodo if you need them.

Labels

*_Label.nii.gz: 0 = background, 1 = vestibular schwannoma, 2 = cochlea (both cochleae share label 2). VS was contoured by the treating neurosurgeon and physicist (radiosurgery planning); cochleae by radiology fellows. Single annotation tier — these masks are the challenge gold standard.

train.jsonl columns

Column Type Meaning
image / mask string repo-relative path prefixed data/nii/CrossMoDA2022/ (loader convention)
label list[str] ["vestibular schwannoma", "cochlea"]
modality / dataset / official_split string "MRI" / "CrossMoDA2022" / "train"
patient_id string filename stem, e.g. crossmoda2022_etz_0
institution string "London" or "Tilburg"
mri_sequence string always "ceT1" (see leaderboard caveat)
in_crossmoda2021 bool duplicate-of-2021 marker
crossmoda2021_id string/null 2021 stem for London, null for Tilburg
koos int/null Koos grade 1-4 (challenge Task 2); null for post-operative cases
post_operative bool 28 London cases; excluded from challenge Task 2

Koos distribution: 1: 12, 2: 51, 3: 73, 4: 46, post-operative: 28 (infos_source_training.csv is the original organizer file).

License

CC BY-NC-SA 4.0, per the organizers' challenge page and the Zenodo record description ("all data"). Note: Zenodo's machine-readable license field auto-tags cc-by-4.0 — a known metadata inconsistency; the NC-SA statement governs. The London subset is separately available upstream under CC BY 4.0 via TCIA (Vestibular-Schwannoma-SEG).

Citation

  • Wijethilake, Dorent, Ivory, Kujawa, Shapey, Cornelissen, Langenhuizen, et al. "crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023." arXiv:2506.12006
  • Dorent et al. "CrossMoDA 2021 challenge." Medical Image Analysis 83:102628,
    1. DOI 10.1016/j.media.2022.102628
  • Shapey et al. "Segmentation of vestibular schwannoma from MRI..." Scientific Data 8:286, 2021. Data DOI 10.7937/TCIA.9YTJ-5Q73