Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,83 @@
+---
+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'
+---
+
+# 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.