Vessel2Graph

Vessel2Graph is a project for skeletonizing and visualizing vessel data from 3D volumes. The project includes scripts for loading data, preprocessing, graph segmentation, visualization, and creating videos of the segmented vessels.

Table of Contents

• Installation
• Usage
• Project Structure
• Scripts
  • main.py
  • data_processing.py
  • graph_segmentation.py
  • visualization.py

Installation

1. Clone the repository:

   git clone https://github.com/ShyuuTheLast/Vessel2Graph.git
   cd Vessel2Graph

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

Command-Line Execution

To run the script from the command line:

1. Prepare your data:

   • Ensure your input data is in HDF5 format.

2. Run the main script:

   python src/main.py input_file.h5 dataset_name --output_file segmented_vessels.h5 --voxel_size 1.0 1.0 1.0 --teasar_params '{"scale": 1.5, "const": 600, "pdrf_scale": 100000, "pdrf_exponent": 4, "soma_acceptance_threshold": 3500, "soma_detection_threshold": 750, "soma_invalidation_const": 300, "soma_invalidation_scale": 2, "max_paths": 300}' --target_labels 1 --segmentation_attribute label --scale_factor 10 --num_rotations 3 --fps 10 --visualize_skeleton --visualize_radii --visualize_skeleton_colored --visualize_paths_radii --create_video --video_graph_type full --video_output_file output_video.avi

IDE Execution

To run the script in an IDE like Spyder or PyCharm:

1. Prepare your data:

   • Ensure your input data is in HDF5 format.

2. Run the main script:

   • Open main.py in your IDE.
   • Manually set the values for the Args class within the script.
   • Execute the script.

Example of the Args class in main.py:

if __name__ == "__main__":
    # Manually set args for testing in an IDE
    class Args:
        input_file = r"C:\Users\14132\Desktop\BC Research Internship\Vessel2Graph\macaque_mpi_176-240nm_bv_gt_cc.h5"  # Path to the input HDF5 file
        dataset_name = 'main'  # Name of the dataset in the HDF5 file
        output_file = 'segmented_macaque_vessels.h5'  # Name of the output HDF5 file
        voxel_size = (320, 256, 256)  # Voxel sizes in z, y, x order
        teasar_params = '{"scale": 1.5, "const": 600, "pdrf_scale": 100000, "pdrf_exponent": 4, "soma_acceptance_threshold": 3500, "soma_detection_threshold": 750, "soma_invalidation_const": 300, "soma_invalidation_scale": 2, "max_paths": 300}'  # JSON string of TEASAR parameters
        target_labels = [1]  # Labels to keep in the array
        segmentation_attribute = 'label'  # Attribute to segment and visualize by
        scale_factor = 30  # Scale factor for visualization
        num_rotations = 3  # Number of full rotations for video
        fps = 10  # Frames per second for video
        visualize_skeleton = True  # Whether to visualize the skeleton
        visualize_radii = True  # Whether to visualize the distribution of radii
        visualize_skeleton_colored = True  # Whether to visualize the skeleton colored by attribute
        visualize_paths_radii = True  # Whether to visualize the radii of paths
        create_video = True  # Whether to create a video
        video_graph_type = 'simplified'  # Type of graph to use for the video ('full' or 'simplified')
        video_output_file = 'simplified_macaque.mp4'  # Name of the output video file (must end with .avi or .mp4)

    args = Args()
    main(args)

Project Structure

• src/: Contains the main scripts for processing and visualization.
• tests/: Contains test scripts.
• README.md: This file.
• requirements.txt: Python dependencies.

Scripts

main.py

The main script orchestrates the entire pipeline from data loading to visualization.

• Arguments:
  • input_file: Path to the input HDF5 file.
  • dataset_name: Name of the dataset in the HDF5 file.
  • output_file: Name of the output HDF5 file.
  • voxel_size: Voxel sizes in z, y, x order.
  • teasar_params: JSON string of TEASAR parameters.
  • target_labels: Labels to keep in the array.
  • segmentation_attribute: Attribute to segment and visualize by.
  • scale_factor: Scale factor for visualization.
  • num_rotations: Number of full rotations for video.
  • fps: Frames per second for video.
  • visualize_skeleton: Whether to visualize the skeleton.
  • visualize_radii: Whether to visualize the distribution of radii.
  • visualize_skeleton_colored: Whether to visualize the skeleton colored by attribute.
  • visualize_paths_radii: Whether to visualize the radii of paths.
  • create_video: Whether to create a video.
  • video_graph_type: Type of graph to use for the video (full or simplified).
  • video_output_file: Name of the output video file (must end with .avi or .mp4).

data_processing.py

Contains functions for loading data, filtering labels, and scaling voxel sizes.

graph_segmentation.py

Contains functions for skeletonizing the volume, generating graphs, identifying branch points, segmenting the volume, and clustering.

visualization.py

Contains functions for visualizing the skeleton, radii, colored skeletons, path radii histograms, and creating videos from graphs.