README.md

---
license: cc-by-nc-sa-4.0
task_categories:
- image-segmentation
tags:
- medical
- ct
- pelvis
- fracture
- bone
- instance-segmentation
- orthopedics
- trauma
- pengwin
- miccai-2024
pretty_name: PENGWIN Task 1 - Pelvic Fracture Segmentation on CT
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: preview
    path: data/preview-*
dataset_info:
  features:
  - name: case_id
    dtype: string
  - name: slice_index
    dtype: int32
  - name: num_slices
    dtype: int32
  - name: orientation_original
    dtype: string
  - name: is_cropped
    dtype: bool
  - name: image_dtype
    dtype: string
  - name: label_dtype
    dtype: string
  - name: spacing_xyz
    list: float32
  - name: n_fragments
    dtype: int32
  - name: n_sacrum_fragments
    dtype: int32
  - name: n_left_hip_fragments
    dtype: int32
  - name: n_right_hip_fragments
    dtype: int32
  - name: labels_on_slice
    list: int32
  - name: hu_min
    dtype: int32
  - name: hu_max
    dtype: int32
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: overlay
    dtype: image
  splits:
  - name: preview
    num_bytes: 14098749
    num_examples: 100
  download_size: 14112067
  dataset_size: 14098749
---

# PENGWIN Task 1 — Pelvic Fracture Segmentation on CT

The **CT task** of the PENGWIN 2024 challenge (*PElvic bone fraGment (WIN)dow*,
MICCAI 2024): segment the **sacrum, left hipbone and right hipbone, and the
individual fracture fragments of each**, in preoperative pelvic trauma CT.

This is an **instance** segmentation task, not a 3-class semantic one — the
label value identifies *which fragment of which bone*, and the fragment count
varies per case.

## What this mirror contains — read first

> **This is the 100-case public training split, not the full 150-case cohort.**
> PENGWIN 2024 used 100 train / 20 validation / 30 test. Only the training split
> was ever released; validation and test were withheld for the leaderboard and
> have not appeared on Zenodo. Any "PENGWIN CT" number quoted as *n=150* refers
> to the paper's cohort, not to available data.

> **Name collision — pin to the 2024 challenge.** A separate **PENGWIN 2026**
> challenge ("Peripelvic Fracture Segmentation and Reduction Planning") exists
> with its own Task 1/2/3 and different Zenodo records. This mirror is
> **Zenodo 10927452, MICCAI 2024**.

> **Not raw scans.** The volumes are de-identified DICOM→MHA conversions, and
> 36 of 100 were cropped to the pelvic region — which is why the geometry varies
> per case (see *Two processing batches* below).

## Dataset Details

| Field | Value |
|---|---|
| Modality | CT (preoperative, before fracture reduction surgery) |
| Body part | Pelvis — sacrum, left hipbone, right hipbone + fracture fragments |
| Task | 3D instance segmentation of bone fragments |
| Cases | **100** (public training split of a 150-case cohort) |
| Cohort | 6 Chinese hospitals, 2017–2023 |
| Format | `.mha` (MetaImage), flat `NNN.mha`, `001`–`100` contiguous |
| Size | 8.08 GB (losslessly compressed; 33.77 GB uncompressed) |
| Slices per case | 193–414 |
| In-plane | 322×154 to 512×512 (71 distinct shapes) |
| Spacing | 0.658–1.22 mm in-plane, 0.625–1.25 mm slice (75 distinct) |
| License | CC BY-NC-SA 4.0 — **see the discrepancy note below** |
| DOI | `10.5281/zenodo.10927452` |

There is **no official validation or test split** in the release, and no
patient/center metadata of any kind. Splitting is left to the consumer; see
*Two processing batches* for the one grouping variable that is recoverable.

## Label encoding

`0` = background. Foreground encodes anatomy **and** fragment index:

| Range | Anatomy |
|---|---|
| `1–10` | Sacrum fragments |
| `11–20` | Left hipbone fragments |
| `21–30` | Right hipbone fragments |

```python
anatomy      = (label - 1) // 10   # 0 sacrum, 1 left hipbone, 2 right hipbone
fragment_idx = (label - 1) %  10   # 0 = main fragment
```

### Verified properties (checked on all 100 label volumes)

These were measured, not taken from the documentation, and several are easy to
get wrong:

- **Observed maximum label is `24`, not `30`.** Values actually present across
  the release: `1–4`, `11–16`, `21–24`. Do not size a one-hot buffer at 30 and
  assume the tail is populated.
- **All three anatomies are present in all 100 cases** — labels `1`, `11` and
  `21` never missing.
- **Groups are contiguous and always start at their base** (`1`/`11`/`21`); no
  gaps in any of the 300 anatomy-groups.
- **The base label is always the largest fragment** in its group (300/300).
  However, the *remaining* fragments are **not** reliably size-ordered —
  45 of 300 groups violate descending order (e.g. `006.mha` sacrum:
  `1`→76023, `2`→52875, `3`→33603, **`4`→68271**). Do not infer size rank from
  the fragment index beyond the main fragment.
- **Fragments per case: 3–9, mean 5.75.**
- **Label dtype is inconsistent: 98 `int16`, 2 `uint8`** (`022.mha`, `072.mha`).
  Do not assume `uint8`.

## ⚠️ Mixed orientation — 34 cases are RAS, 66 are LPS

**This is the single easiest thing to get wrong with this dataset.**

| Direction cosines | n | Orientation |
|---|---|---|
| `diag(+1, +1, +1)` | 66 | LPS |
| `diag(−1, −1, +1)` | **34** | **RAS** |

No case is genuinely oblique — it is a clean ±1 flip on x and y.

Image and label share identical direction in **every** case, so per-case overlap
metrics stay correct even if you ignore this. But a loader that calls
`GetArrayFromImage()` without consulting the direction cosines will get **34
cases left–right and anterior–posterior flipped relative to the other 66**. The
consequence is semantic: labels `11–20` are the *left* hipbone anatomically, but
land on **opposite sides of the array** depending on the case. Any model with a
left/right prior, and any evaluation that treats `11–20` as a consistent class,
is silently corrupted.

**Canonicalize before use:**

```python
import SimpleITK as sitk
img = sitk.DICOMOrient(sitk.ReadImage("images/001.mha"), "LPS")
msk = sitk.DICOMOrient(sitk.ReadImage("labels/001.mha"), "LPS")
```

The per-case `orientation` column in `train.jsonl` records which is which.

## Two processing batches

Orientation is a near-perfect proxy for whether a volume was cropped:

| | 512×512 in-plane | Cropped in-plane |
|---|---|---|
| **LPS** (66) | 64 | 2 |
| **RAS** (34) | **0** | **34** |

Every RAS case is cropped (each to a distinct matrix size); 64 of 66 LPS cases
are untouched 512×512. Image dtype correlates too — 79% of RAS cases are `int32`
versus 39% of LPS. This matches the Zenodo note that volumes containing extra
anatomy "were cropped to contain the pelvic region": that second pass evidently
also rewrote orientation.

So the 100 cases are **two sub-populations produced by different pipelines**.
This is the only grouping variable the release exposes and is worth stratifying
on. It is **not** a recovery of the 6-hospital split — PENGWIN publishes no
center labels, and this correlation identifies *processing batch*, nothing more.

## Image properties

- **Image dtype is inconsistent: 53 `int32`, 47 `int16`.** HU values fit
  comfortably in `int16`; the `int32` cases are simply stored wider. This mirror
  **preserves the original dtype** rather than downcasting.
- Intensity ranges are wide (down to −6152, up to +24970 HU in some cases),
  consistent with trauma cohorts containing implants and metal.
- **Image and label share an identical grid** (size, spacing, origin, direction)
  in all 100 cases — verified — so no resampling is needed to pair them.

## Ground truth — single gold tier

Two independent annotators (5+ years' experience) segmented each case in 3D
Slicer, **seeded by an nnU-Net pretrained on CTPelvic1K**, after which a senior
expert (15+ years) **selected the better of the two annotations** — they were not
merged, and no STAPLE was applied. Fragments below 500 mm³ were omitted.
Reported inter-annotator agreement: IoU 0.984, ARI 0.993.

Only one mask per case ships, so there is **no multi-rater tier** in this
release and no rater ambiguity to resolve.

## ⚠️ Cross-dataset overlap — CTPelvic1K

**Treat PENGWIN Task 1 and CTPelvic1K as potentially patient-overlapping.**

CTPelvic1K's `CLINIC` subset is **n=103** pelvic-fracture CT "collected from
preoperative images without metal artifact" at a collaborating orthopedic
hospital. PENGWIN's Beijing Jishuitan center contributed **n=103** scans
"acquired in high quality before fracture reduction surgery". **Chunpeng Zhao and
Xinbao Wu co-author both papers.** Identical count, identical hospital,
identical inclusion criteria.

Against exact identity: the scanner mix differs (CTPelvic1K's CLINIC is roughly
86 Toshiba + ~17 other; PENGWIN's JST is 58 Toshiba + 45 United Imaging), and
PENGWIN spans 2017–2023, past CTPelvic1K's 2020 curation. Neither paper
acknowledges any overlap.

**Conclusion: not identical, but drawn from the same archive over an overlapping
window. Partial patient overlap is likely and cannot be excluded from published
metadata.** There is **no cross-reference ID** — both releases use anonymized
sequential IDs (`001.mha`–`100.mha` vs `dataset6_CLINIC_0001`–`0103`) and PENGWIN
ships no patient, center or scanner fields. Deduplication would have to be
content-based (match on spacing and slice count, then cross-correlate mid-axial
slices within the overlapping FOV).

Two further leakage notes:

1. **The ground truth is partly a function of CTPelvic1K.** PENGWIN's annotations
   were seeded by an nnU-Net trained on CTPelvic1K, so the two label sets are not
   statistically independent even where the patients differ.
2. **PENGWIN Task 2 X-rays are DeepDRR renderings of these same CT volumes.**
   Using both tasks together creates internal patient overlap by construction.

**No overlap** with TotalSegmentator (Basel, routine whole-body CT) or VerSe
(European multi-center spine CT). PENGWIN CT is newly collected Chinese hospital
trauma data and shares nothing with CTPelvic1K's *public-archive* lineage
(COLONOG / KITS19 / MSD-T10 / ABDOMEN / CERVIX).

## ⚠️ License discrepancy

| Source | States |
|---|---|
| Zenodo record 10927452 metadata | **CC BY 4.0** (`cc-by-4.0`, open access) |
| PENGWIN challenge report text | **CC BY-NC-SA** |

These contradict. The same team has the mirror-image discrepancy on CTPelvic1K
(paper says CC BY-NC-SA 4.0, Zenodo 4588403 says CC BY 4.0), so it appears
systematic rather than a typo.

This mirror declares the **more restrictive, author-stated CC BY-NC-SA 4.0** so
that use is safe under either reading. Both licenses permit redistribution. If
you need commercial or non-ShareAlike terms, consult the Zenodo record and
contact the organizers rather than relying on this choice.

## Structure

```
images/NNN.mha        # 100 CT volumes   (001-100)
labels/NNN.mha        # 100 instance masks, same grid as the image
train.jsonl           # per-case metadata, one JSON object per line
README.md
LICENSE.txt
```

`train.jsonl` columns:

| Column | Meaning |
|---|---|
| `case_id` | `"001"` … `"100"` |
| `image`, `mask` | repo-relative paths |
| `split` | always `"train"` (no official val/test released) |
| `shape_zyx`, `spacing_xyz`, `origin_xyz` | geometry |
| `orientation` | `"LPS"` or `"RAS"` — **see the orientation warning** |
| `is_cropped` | `true` if in-plane is not 512×512 |
| `image_dtype`, `label_dtype` | original dtypes (both are mixed) |
| `hu_min`, `hu_max` | intensity range |
| `label_values` | sorted foreground labels present |
| `n_fragments` | total fragments |
| `n_sacrum_fragments`, `n_left_hip_fragments`, `n_right_hip_fragments` | per-anatomy counts |
| `fragment_voxels` | `{label: voxel_count}` |

## Storage note

The `.mha` files are rewritten with lossless zlib compression (33.77 GB → 8.08
GB, 4.18×). Voxel arrays, dtype, spacing, origin and direction were verified
**bit-identical to the Zenodo originals on all 200 files** (`np.array_equal`,
exact, after a fresh re-read from disk). `.mha` compression is transparent to
ITK/SimpleITK — no change to how you read the files.

## Source & Citation

- Zenodo: https://doi.org/10.5281/zenodo.10927452 (open, no registration, no DUA)
- Challenge: https://pengwin.grand-challenge.org/ (an account is needed only for
  leaderboard submission, not for the data)

```bibtex
@article{sang2026pengwin,
  author  = {Sang, Yudi and Liu, Yanzhen and Yibulayimu, Sutuke and others},
  title   = {Benchmark of Segmentation Techniques for Pelvic Fracture in CT and
             X-Ray: Summary of the PENGWIN 2024 Challenge},
  journal = {IEEE Transactions on Medical Imaging},
  year    = {2026},
  doi     = {10.1109/TMI.2025.3650126}
}

@inproceedings{liu2023pelvic,
  author    = {Liu, Yanzhen and Yibulayimu, Sutuke and Sang, Yudi and Zhu, Gang
               and Wang, Yu and Zhao, Chunpeng and Wu, Xinbao},
  title     = {Pelvic Fracture Segmentation Using a Multi-scale Distance-Weighted
               Neural Network},
  booktitle = {MICCAI 2023},
  pages     = {312--321},
  year      = {2023},
  doi       = {10.1007/978-3-031-43996-4_30}
}

@article{liu2025automatic,
  author  = {Liu, Yanzhen and Yibulayimu, Sutuke and Zhu, Gang and others},
  title   = {Automatic pelvic fracture segmentation: a deep learning approach
             and benchmark dataset},
  journal = {Frontiers in Medicine},
  volume  = {12},
  pages   = {1511487},
  year    = {2025},
  doi     = {10.3389/fmed.2025.1511487}
}
```