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---
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/CHASEDB1_12R_rgb.png" width="240" height="240"><img src="imgs/CHASEDB1_12R.png" width="240" height="240">
<img src="imgs/DRIVE_22_rgb.png" width="240" height="240"><img src="imgs/DRIVE_22.png" width="240" height="240">
<img src="imgs/HRF_04_g_rgb.png" width="240" height="240"><img src="imgs/HRF_04_g.png" width="240" height="240">
### 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).
### Usage
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.
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