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PPCNet Dataset

Biplanar DRRs + Projection Matrices + Ground-Truth Point Clouds

License Patients Points Size

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:

  1. Surface extraction — marching cubes on per-vertebra segmentation masks
  2. Coordinate transform — vertices mapped to physical (mm) space via NIfTI affine
  3. Concatenation — all five vertebrae (L1–L5) merged into a single cloud
  4. Alignment — rigid ICP with four flip initialisations to match DRR projection space
  5. 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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