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--- |
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license: agpl-3.0 |
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pipeline_tag: image-segmentation |
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tags: |
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- medical |
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- biology |
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--- |
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## VascX models |
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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). |
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The model weights are in [huggingface](https://huggingface.co/Eyened/vascx). |
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<img src="imgs/CHASEDB1_12R_rgb.png" width="240" height="240" style="display:inline"><img src="imgs/CHASEDB1_12R.png" width="240" height="240" style="display:inline"> |
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<img src="imgs/DRIVE_22_rgb.png" width="240" height="240" style="display:inline"><img src="imgs/DRIVE_22.png" width="240" height="240" style="display:inline"> |
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<img src="imgs/HRF_04_g_rgb.png" width="240" height="240" style="display:inline"><img src="imgs/HRF_04_g.png" width="240" height="240" style="display:inline"> |
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### Installation |
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To install the entire fundus analysis pipeline including fundus preprocessing, model inference code and vascular biomarker extraction: |
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1. Create a conda or virtualenv virtual environment, or otherwise ensure a clean environment. |
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2. Install the [rtnls_inference package](https://github.com/Eyened/retinalysis-inference). |
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### Usage |
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To speed up re-execution of vascx we recommend to run the preprocessing and segmentation steps separately: |
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1. Preprocessing. See [this notebook](./notebooks/0_preprocess.ipynb). This step is CPU-heavy and benefits from parallelization (see notebook). |
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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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