CT Binary Coronary Segmentation

Binary coronary artery segmentation from cardiac CT angiography (CCTA). Produces a binary vessel mask that can be used standalone or as input to the segmental coronary bundle.

Architecture: UNet [64,128,256,512], 1 input channel (CT), 2 output channels (background + vessel) Spacing: 0.6 mm isotropic

Quick Start (Inference)

# Download from HuggingFace
pip install huggingface_hub
huggingface-cli download kbressem/ct-heart-segmentation \
  --include "ct_binary_coronary_segmentation/**" \
  --local-dir ./

cd ct_binary_coronary_segmentation

# Single model (fold 0)
python -m monai.bundle run inference \
  --config_file configs/inference.yaml \
  --dataset_dir /path/to/ct_images \
  --output_dir /path/to/output

# 5-fold ensemble (recommended)
python -m monai.bundle run inference \
  --config_file configs/ensemble_inference.yaml \
  --dataset_dir /path/to/ct_images \
  --output_dir /path/to/output

# Use a specific fold
python -m monai.bundle run inference \
  --config_file configs/inference.yaml \
  --dataset_dir /path/to/ct_images \
  --fold 2

Input: Directory of CT images in NIfTI (.nii, .nii.gz), NRRD (.nrrd), or DICOM (.dcm) format.

Output: Per-image binary segmentation mask (NIfTI) resampled to original spacing, plus a _centerline.json file containing spline-smoothed vessel centerlines in physical (mm) coordinates.

Requirements

monai>=1.3.0
torch>=2.0
scikit-image
scipy
pandas
psutil
itk

Configuration Files

File	Purpose
`configs/inference.yaml`	Single-model inference
`configs/ensemble_inference.yaml`	5-fold ensemble inference (best quality)
`configs/train.yaml`	Training config (requires local data manifest)
`configs/metadata.json`	Bundle metadata

Model Weights

5-fold cross-validation weights in models/:

ct_binary_coronary_segmentation_unet_fold_0.pt through fold_4.pt

Preprocessing Pipeline

LoadImaged (ITKReader) -> EnsureChannelFirstd -> Spacingd (0.6mm isotropic)
-> GaussianSmoothd (sigma=0.5) -> ApplyWindowingd (-100 to 900 HU)
-> ZScoreForegroundNormalized (threshold=-50)

Scripts

scripts/transforms.py - Custom transforms (windowing, normalization, centerline extraction)
scripts/utils.py - Data loading and inference helpers
scripts/losses.py - DiceclDiceLoss (Dice + centerline Dice)
scripts/meta.py - Metadata preservation transforms