--- 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} } ```