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PPCNet Dataset
Biplanar DRRs + Projection Matrices + Ground-Truth Point Clouds
A curated lumbar spine dataset for 3D point cloud reconstruction from biplanar radiographs.
Overview
This dataset provides paired biplanar DRRs, calibrated 3×4 projection matrices, and dense ground-truth point clouds for 1,037 patients with complete L1–L5 lumbar vertebrae, derived from the publicly available VerSe'19, VerSe'20, and CTSpine1K collections.
📁 Dataset Structure
Each of the 1,037 patient folders contains:
| File | Description |
|---|---|
ct.nii.gz |
CT volume (LPS orientation) |
seg.nii.gz |
Segmentation labels (L1=20, L2=21, L3=22, L4=23, L5=24) |
gt_ppc.vtk |
Ground-truth point cloud (5,120 points, world-mm) |
AP_0/drr_AP_0.png |
Anteroposterior DRR (512×512) |
AP_0/P_AP_0.txt |
3×4 AP projection matrix |
LP_90/drr_LP_90.png |
Lateral DRR (512×512) |
LP_90/P_LP_90.txt |
3×4 lateral projection matrix |
Label mapping: L1=20, L2=21, L3=22, L4=23, L5=24
Data Split
Fixed split in dataset_split.json (seed=42):
| Split | Patients | Usage |
|---|---|---|
| Train | 829 | Model training |
| Validation | 103 | Hyperparameter tuning |
| Test | 105 | Final evaluation |
| Total | 1,037 |
DRR Generation
DRRs are generated using Plastimatch's ray-casting algorithm with the following parameters:
| Parameter | Value |
|---|---|
| Source-to-Axis Distance (SAD) | 1,000 mm |
| Source-to-Imager Distance (SID) | 1,500 mm |
| Detector Size | 500 × 500 mm |
| Image Resolution | 512 × 512 px |
| Views | AP (0°) + Lateral (90°) |
| Bone Enhancement | 2.5× for HU > 300 |
| Post-processing | CLAHE normalisation |
Each DRR comes with a calibrated 3×4 perspective projection matrix encoding the exact source–detector geometry.
Ground-Truth Generation
The ground-truth point clouds are constructed through a five-step pipeline:
- Surface extraction — marching cubes on per-vertebra segmentation masks
- Coordinate transform — vertices mapped to physical (mm) space via NIfTI affine
- Concatenation — all five vertebrae (L1–L5) merged into a single cloud
- Alignment — rigid ICP with four flip initialisations to match DRR projection space
- Sampling — farthest-point sampling to a uniform 5,120-point representation
⬇️ Download
from huggingface_hub import snapshot_download
# Download full dataset (~69.2 GB)
snapshot_download(
repo_id="ppcnet-dataset/PPCNet",
repo_type="dataset",
local_dir="./PPCNet_Dataset"
)
Or use the Hugging Face CLI:
pip install huggingface_hub
huggingface-cli download ppcnet-dataset/PPCNet --repo-type dataset --local-dir ./PPCNet_Dataset
Source Datasets
This dataset builds upon:
- 📦 VerSe — A Vertebrae Labelling and Segmentation Benchmark for Multi-Detector CT Images
- 📦 CTSpine1K — A Large-Scale Dataset for Spinal Vertebrae Segmentation in CT
License
This dataset is released under the MIT License.
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