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README.md

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+---
+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
+---
+
+# 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}
+}
+```