---
license: agpl-3.0
pipeline_tag: image-segmentation
tags:
- medical
- biology
---

# 👁️ 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](https://arxiv.org/abs/2409.16016).

The model weights are in [huggingface](https://huggingface.co/Eyened/vascx).

<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](https://github.com/Eyened/retinalysis-inference).


## 🚀 `vascx run` Command

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

### Usage

```bash
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

| Option | Default | Description |
|--------|---------|-------------|
| `--preprocess/--no-preprocess` | `--preprocess` | Run preprocessing to standardize images for model input |
| `--vessels/--no-vessels` | `--vessels` | Run vessel segmentation and artery-vein classification |
| `--disc/--no-disc` | `--disc` | Run optic disc segmentation |
| `--quality/--no-quality` | `--quality` | Run image quality assessment |
| `--fovea/--no-fovea` | `--fovea` | Run fovea detection |
| `--overlay/--no-overlay` | `--overlay` | Create visualization overlays combining all results |
| `--n_jobs` | `4` | Number of preprocessing workers for parallel processing |

### 📁 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:**
```bash
vascx run /path/to/images /path/to/output
```

**Process specific images listed in a CSV:**
```bash
vascx run /path/to/image_list.csv /path/to/output
```

**Only run preprocessing and vessel segmentation:**
```bash
vascx run /path/to/images /path/to/output --no-disc --no-quality --no-fovea --no-overlay
```

**Skip preprocessing on already preprocessed images:**
```bash
vascx run /path/to/preprocessed/images /path/to/output --no-preprocess
```

**Increase parallel processing workers:**
```bash
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](./notebooks/0_preprocess.ipynb). This step is CPU-heavy and benefits from parallelization (see notebook).

2. Inference. See [this notebook](./notebooks/1_segment_preprocessed.ipynb). All models can be ran in a single GPU with >10GB VRAM.