| --- |
| license: cc-by-nc-sa-4.0 |
| task_categories: |
| - image-segmentation |
| tags: |
| - medical |
| - x-ray |
| - pelvis |
| - fracture |
| - synthetic |
| - multi-label |
| pretty_name: 'PENGWIN Task 2: Pelvic Fragment Segmentation on Synthetic X-ray Images' |
| dataset_info: |
| features: |
| - name: image_display |
| dtype: image |
| - name: overlay |
| dtype: image |
| - name: image |
| dtype: image |
| - name: mask |
| dtype: image |
| - name: image_id |
| dtype: string |
| - name: case_id |
| dtype: int32 |
| - name: projection_index |
| dtype: int32 |
| - name: has_hardware |
| dtype: bool |
| - name: fragment_labels |
| list: int32 |
| - name: n_fragments |
| dtype: int32 |
| - name: n_sa |
| dtype: int32 |
| - name: n_li |
| dtype: int32 |
| - name: n_ri |
| dtype: int32 |
| splits: |
| - name: train |
| num_bytes: 37941083347 |
| num_examples: 50000 |
| download_size: 37948398318 |
| dataset_size: 37941083347 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| --- |
| |
| # PENGWIN Task 2: Pelvic Fragment Segmentation on Synthetic X-ray Images |
|
|
| Mirror of the **training split** of Task 2 of the MICCAI 2024 PENGWIN challenge |
| (https://pengwin.grand-challenge.org/), from the official Zenodo record |
| [10913196](https://zenodo.org/records/10913196) (`train.zip`, md5 `9c90215dae54d8f494a85cfc7b19bc96`). |
|
|
| **These are SYNTHETIC X-rays, not real radiographs**: DeepDRR renders of the 100 PENGWIN |
| Task 1 training CTs simulating intraoperative C-arm fluoroscopy, 500 random poses per CT |
| = **50,000 image/mask pairs**. Projections `0000-0249` show clean anatomy; `0250-0499` |
| additionally contain up to 10 simulated K-wires/orthopaedic screws (`has_hardware`). |
| The challenge validation (8,000) and test (600) X-rays were never publicly released. |
| No real radiographs exist anywhere in PENGWIN 2024. (Zenodo's description writes the |
| hardware range as `0250-0500`; indices verifiably end at `0499`.) |
|
|
| ## Columns |
|
|
| | Column | Content | |
| |---|---| |
| | `image_display` | uint8 JPEG, official `visualize_drr` rendering (neg-log -> CLAHE -> invert). Browsing aid, lossy. | |
| | `overlay` | RGB JPEG, per-fragment color fill + contour on `image_display`. Browsing aid, lossy. | |
| | `image` | **Raw float32 448x448 DRR** (lossless deflate TIFF, pixel-identical to Zenodo). Intensities are pre-neg-log; apply `-log` + windowing before use (see below). | |
| | `mask` | **uint32 bit-encoded multi-label segmentation** (lossless deflate TIFF; stored int32, values < 2^31, pixel-identical to Zenodo). NOT a plain label map. | |
| | `image_id` | `{case:03d}_{projection:04d}` | |
| | `case_id` | Source CT case 1-100 == the same patient's `PENGWIN_Task1` volume (see Overlap) | |
| | `projection_index` | 0-499 | |
| | `has_hardware` | `projection_index >= 250` | |
| | `fragment_labels` | Fragment labels present (set bit positions 1-30) | |
| | `n_fragments`, `n_sa`, `n_li`, `n_ri` | Fragment counts (total / sacrum / left hipbone / right hipbone) | |
|
|
| ## Mask encoding |
|
|
| A pixel's uint32 value has bit `b = 10*(category-1) + fragment` set iff that fragment |
| projects onto the pixel (categories: 1 sacrum SA, 2 left hipbone LI, 3 right hipbone RI; |
| fragments 1-10, fragment 1 = main). **Overlapping fragments are the norm** (X-ray |
| projection superimposes bone), so decode to per-fragment binary masks - do not treat the |
| value as a class ID. Bit 0 is never set. Bit `b` corresponds exactly to label value `b` |
| in `MedOtter/PENGWIN_Task1`. |
|
|
| ```python |
| import numpy as np |
| masks = [((seg >> b) & 1).astype(bool) for b in range(1, 31) if ((seg >> b) & 1).any()] |
| ``` |
|
|
| The official `pengwin_utils.py` (this repo's root, from the Zenodo record) provides |
| `seg_to_masks` / `masks_to_seg`, the DRR renderer, and the challenge augmentation |
| pipeline. Official deterministic test-time input: `neglog` then quantile window |
| `(0.01, 0.95)` (see `build_augmentation(train=False)`). |
|
|
| ## Overlap warning |
|
|
| Derived from **exactly the 100 CTs in `MedOtter/PENGWIN_Task1`** (same case numbering, |
| same patients) - never treat the two as independent benchmarks. Task 1 in turn likely |
| shares patients with CTPelvic1K's CLINIC subset (no ID crosswalk exists), and its GT was |
| seeded by a CTPelvic1K-pretrained nnU-Net. Do not confuse with the separate PENGWIN 2026 |
| challenge, whose "Task 2" is a different task on different data. |
| |
| ## License |
| |
| The Zenodo record metadata declares CC BY 4.0, while the challenge summary paper's Data |
| Availability statement says the X-ray training set is released under **CC BY-NC-SA** - the |
| same record-vs-paper conflict as PENGWIN Task 1. As with our Task 1 mirror, this mirror |
| adopts the stricter author-stated **CC BY-NC-SA 4.0**. |
| |
| ## Citation |
| |
| Sang Y. et al., "Benchmark of Segmentation Techniques for Pelvic Fracture in CT and |
| X-Ray: Summary of the PENGWIN 2024 Challenge," IEEE TMI, doi:10.1109/TMI.2025.3650126 |
| (arXiv:2504.02382). Data: doi:10.5281/zenodo.10913196. Lineage: Liu Y. et al., MICCAI |
| 2023, doi:10.1007/978-3-031-43996-4_30. |
| |
