🫀 CoroSAM

Interactive Coronary Artery Segmentation with Segment Anything Model

📄 Paper • 🚀 Getting Started • 💾 Pretrained Models • 🎮 GUI

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CoroSAM is a deep learning framework for interactive coronary artery segmentation in coronary angiograms, built on a computationally efficient SAM-based architecture with custom convolutional adapters.

This is the official implementation of the paper published in Computer Methods and Programs in Biomedicine.

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📑 Table of Contents

• Installation
• Pretrained checkpoints
• ARCADE dataset
• Preprocessing
• Training
• Testing
• Testing on different datasets
• GUI application
• Citation
• Acknowledgments

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Installation

1. Create virtual environment & install PyTorch

First, install PyTorch following the official installation guide.

Recommended version: torch==2.6.0+cu124

2. Clone repository

git clone https://github.com/mife-git/corosam.git
cd corosam

3. Install dependencies

pip install -r requirements.txt

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Pretrained checkpoints

External

Download and place in checkpoints/Pretrained/:

Model	Source	Path
LiteMedSAM	GitHub	checkpoints/Pretrained/lite_medsam.pth
SAMMed2D	GitHub	checkpoints/Pretrained/sam-med2d_b.pth

CoroSAM

Our pretrained CoroSAM model trained on ARCADE is available here:

📥 Download CoroSAM Checkpoint

Save as: checkpoints/CoroSAM/CoroSAM_Final_Training.pt

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ARCADE dataset preparation

Download

1. Download ARCADE from Zenodo
2. Extract to your workspace
3. Use only the syntax subset for this project

arcade/
└── syntax/
    ├── train/
    ├── val/
    └── test/

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Preprocessing

Transform ARCADE COCO annotations into training-ready format.

1. Configure paths

Edit preprocessing_config.yaml:

dataset_root: "C:/path/to/arcade/syntax"
seed: 2025

2. Run preprocessing pipeline

# Step 1: Convert COCO to binary masks
python preprocessing/convert_coco_to_binary_masks.py

# Step 2: Merge train+val and apply augmentation
python preprocessing/data_augmentation.py

# Step 3: Create k-fold splits
python preprocessing/split_dataset.py

Output structure:

syntax/
├── train/              # Original train set
├── val/                # Original val set
├── test/               # Test set
├── train_all/          # Merged train+val
│   ├── images/
│   ├── annotations/
│   ├── images_augmented/
│   └── annotations_augmented/
└── kf_split/           # 5-fold cross-validation
    ├── set1/
    ├── set2/
    └── ...

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Training

Train CoroSAM on your data with flexible configurations.

Configuration

Edit train_config.yaml:

# Dataset
dataset_root: "C:/path/to/arcade/syntax"
k_fold_path: "C:/path/to/arcade/syntax/kf_split"

# Model
model_name: "LiteMedSAM"
exp_name: "CoroSAM_Training"

# Adapters
use_adapters: true
use_conv_adapters: true
channel_reduction: 0.25

# Training
n_folds: 5        # 5-fold CV or set to 1 for single run
epochs: 25
batch_size: 4
lr: 0.0005

# Logging
use_wandb: true
proj_name: "CoroSAM"

Run training

K-fold cross-validation:

python training/train.py --config train_config.yaml

Single training run:

n_folds: 1
train_path: "C:/path/to/arcade/syntax/train_all"
val_path: "C:/path/to/arcade/syntax/test"

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Testing

Comprehensive evaluation with detailed metrics and visualizations.

Configure testing

Edit test_config.yaml:

# Model
model_name: "LiteMedSAM"
checkpoint: "checkpoints/CoroSAM/CoroSAM_Final_Training.pt"

# Dataset
test_path: "C:/path/to/arcade/syntax/test"
results_path: "results/CoroSAM_ARCADE_Test"

# Options
save_predictions: true  # Save visualization images

Run testing

python testing/test.py --config test_config.yaml

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Testing on different datasets

CoroSAM can be evaluated on any custom dataset!

Requirements

Your dataset must follow the ARCADE preprocessing output structure:

dataset_name/
└── test/  (or any folder name)
    ├── images/
    │   ├── dataset_name_1.png
    │   ├── dataset_name_2.png
    │   └── ...
    └── annotations/
        ├── dataset_name_1_gt.png
        ├── dataset_name_2_gt.png
        └── ...

Quick test

# test_config.yaml
test_path: "path/to/your_dataset/test"
checkpoint: "checkpoints/CoroSAM/corosam_pretrained.pth"

python testing/test.py --config test_config.yaml

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GUI application

Interactive segmentation with a user-friendly interface.

Launch GUI

python gui/gui_corosam.py

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Citation

If you find CoroSAM useful in your research, please cite our paper:

@article{corosam2025,
  title={CoroSAM: adaptation of the Segment Anything Model for interactive segmentation in Coronary angiograms},
  journal={Computer Methods and Programs in Biomedicine},
  year={2025},
  publisher={Elsevier},
  doi={10.1016/j.cmpb.2025.108587},
  url={https://www.sciencedirect.com/science/article/pii/S0169260725005887}
}

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Acknowledgments

This project builds upon excellent open-source work:

• Segment Anything Model (SAM): facebookresearch/segment-anything
• MedSAM: bowang-lab/MedSAM
• SAM-Med2D: OpenGVLab/SAM-Med2D