vascx

[!IMPORTANT] The entire VascX pipeline, including feature extraction is available here: retinalysis-vascx. A new preprint presenting the pipeline is available here. We will stop supporting this repository and move to the new one linked above. We will keep updating the models and pipeline there.

👁️ VascX models

This repository contains the instructions for using the VascX models from the paper VascX Models: Model Ensembles for Retinal Vascular Analysis from Color Fundus Images.

The model weights are in huggingface.

<img src="imgs/samples_vascx_hrf.png">

🛠️ Installation

To install the entire fundus analysis pipeline including fundus preprocessing, model inference code and vascular biomarker extraction:

1. Create a conda or virtualenv virtual environment, or otherwise ensure a clean environment.

2. Install the rtnls_inference package.

🚀 vascx run Command

The run command provides a comprehensive pipeline for processing fundus images, performing various analyses, and creating visualizations.

Usage

vascx run DATA_PATH OUTPUT_PATH [OPTIONS]

Arguments

• DATA_PATH: Path to input data. Can be either:

  • A directory containing fundus images
  • A CSV file with a 'path' column containing paths to images

• OUTPUT_PATH: Directory where processed results will be stored

Options

📁 Output Structure

When run with default options, the command creates the following structure in OUTPUT_PATH:

OUTPUT_PATH/
├── preprocessed_rgb/     # Standardized fundus images
├── vessels/              # Vessel segmentation results
├── artery_vein/          # Artery-vein classification
├── disc/                 # Optic disc segmentation
├── overlays/             # Visualization images
├── bounds.csv            # Image boundary information
├── quality.csv           # Image quality scores
└── fovea.csv             # Fovea coordinates

🔄 Processing Stages

1. Preprocessing:

   • Standardizes input images for consistent analysis
   • Outputs preprocessed images and boundary information

2. Quality Assessment:

   • Evaluates image quality with three quality metrics (q1, q2, q3)
   • Higher scores indicate better image quality

3. Vessel Segmentation and Artery-Vein Classification:

   • Identifies blood vessels in the retina
   • Classifies vessels as arteries (1) or veins (2) with intersections (3)

4. Optic Disc Segmentation:

   • Identifies the optic disc location and boundaries

5. Fovea Detection:

   • Determines the coordinates of the fovea (center of vision)

6. Visualization Overlays:

   • Creates color-coded images showing:
     • Arteries in red
     • Veins in blue
     • Optic disc in white
     • Fovea marked with yellow X

💻 Examples

Process a directory of images with all analyses:

vascx run /path/to/images /path/to/output

Process specific images listed in a CSV:

vascx run /path/to/image_list.csv /path/to/output

Only run preprocessing and vessel segmentation:

vascx run /path/to/images /path/to/output --no-disc --no-quality --no-fovea --no-overlay

Skip preprocessing on already preprocessed images:

vascx run /path/to/preprocessed/images /path/to/output --no-preprocess

Increase parallel processing workers:

vascx run /path/to/images /path/to/output --n_jobs 8

📝 Notes

• The CSV input must contain a 'path' column with image file paths
• If the CSV includes an 'id' column, these IDs will be used instead of filenames
• When --no-preprocess is used, input images must already be in the proper format
• The overlay visualization requires at least one analysis component to be enabled

📓 Notebooks

For more advanced usage, we have Jupyter notebooks showing how preprocessing and inference are run.

To speed up re-execution of vascx we recommend to run the preprocessing and segmentation steps separately:

1. Preprocessing. See this notebook. This step is CPU-heavy and benefits from parallelization (see notebook).

2. Inference. See this notebook. All models can be ran in a single GPU with >10GB VRAM.