--Code for AutoMorph: Automated Retinal Vascular Morphology Quantification via a Deep Learning Pipeline.
Please contact [email protected] or [email protected] if you have questions.
Project website: https://rmaphoh.github.io/projects/automorph.html
Talks on NIHR Moorfields BRC: https://moorfieldsbrc.nihr.ac.uk/case-study/research-report/
2024-06-27 update: pytorch 2.3 & python 3.11 supported; Mac M2 GPU supported; CPU supported (thanks to staskh)
2023-08-24 update: Added feature measurement for disc-centred images; removed unused files.
The units for vessel average width, disc/cup height and width, and calibre metrics are defined as microns. For it, we need to organise a resolution_information.csv which includes the pixel resolution information, which can be queried in FDA or Dicom files. Alternatively, approximate value can be used, e.g., 0.008 for Topcon 3D-OCT.
If you don't use these features or care their units, you can put all images in the folder ./images and run
python generate_resolution.py
Use the Google Colab and a free Tesla T4 gpu Colab link click.
Install and use on your own machines LOCAL.md.
Zero experience in Docker? No worries DOCKER.md.
We use Tesla T4 (16Gb) and 32vCPUs (120Gb). When you meet memory/ram issue in running, try to decrease batch size:
- ./M1_Retinal_Image_quality_EyePACS/test_outside.sh -b=64 to smaller, e.g., 32 or 16.
- ./M2_Artery_vein/test_outside.sh --batch-size=8 to smaller
- ./M2_lwnet_disc_cup/test_outside.sh --batchsize=8 to smaller
In csv files, invalid values (e.g., optic disc segmentation failure) are indicated with -1.
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Vessel segmentation BF-Net
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Image pre-processing EyeQ
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Optic disc segmentation lwnet
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Feature measurement retipy
@article{zhou2022automorph,
title={AutoMorph: Automated Retinal Vascular Morphology Quantification Via a Deep Learning Pipeline},
author={Zhou, Yukun and Wagner, Siegfried K and Chia, Mark A and Zhao, An and Xu, Moucheng and Struyven, Robbert and Alexander, Daniel C and Keane, Pearse A and others},
journal={Translational vision science \& technology},
volume={11},
number={7},
pages={12--12},
year={2022},
publisher={The Association for Research in Vision and Ophthalmology}
}
