--Vessel segmentation, artery and vein, retinal image
--Code for MICCAI paper "Learning to Address Intra-segment Misclassification in Retinal Imaging"
Please contact ykzhoua@gmail.com or yukun.zhou.19@ucl.ac.uk if you have questions.
This repository aims at improving multi-class vessel segmentation performance in retinal fundus photograph by alleviating the intra-segment misclassification around the intersections. The research data sets in experiments include DRIVE-AV 1,2, LES-AV 3, and HRF-AV 4,5.
There are a few strengths in this work:
scripts.utils.py
Packages installation:
pip install -r requirements.txt
The pretrained model are provided in Google_DRIVE. Download them and unzip them directly at the project folder.
Start training, the dataset can be set as DRIVE_AV, LES-AV, or HRF-AV.
python train.py --e=500 \
--batch-size=2 \
--learning-rate=8e-4 \
--v=10.0 \
--alpha=0.5 \
--beta=1.1 \
--gama=0.08 \
--dataset=DRIVE_AV \
--discriminator=unet \
--job_name=DRIVE_AV_randomseed_42 \
--uniform=True \
--seed_num=42
Test the trained models.
python test.py --batch-size=1 \
--dataset=DRIVE_AV \
--job_name=DRIVE_AV_randomseed \
--uniform=True
Test dataset | Sensitivity | AUC-ROC | F1-score | AUC-PR | MSE |
---|---|---|---|---|---|
DRIVE-AV | 70.8 ± 0.1 | 84.7 ± 0.05 | 71.99 ± 0.04 | 73.06 ± 0.03 | 2.85 ± 0.01 |
LES-AV | 64.41 ± 0.09 | 81.72 ± 0.04 | 67.22 ± 0.06 | 69.08 ± 0.06 | 2.22 ± 0.01 |
HRF-AV | 71.85 ± 0.29 | 85.38 ± 0.13 | 71.92 ± 0.03 | 73.23 ± 0.03 | 2 ± 0.01 |
1) Staal J, Abrà moff M D, Niemeijer M, et al. Ridge-based vessel segmentation in color images of the retina[J]. IEEE transactions on medical imaging, 2004, 23(4): 501-509.
2) Hu Q, Abrà moff M D, Garvin M K. Automated separation of binary overlapping trees in low-contrast color retinal images[C]//International conference on medical image computing and computer-assisted intervention. Springer, Berlin, Heidelberg, 2013: 436-443.
3) Orlando J I, Breda J B, Van Keer K, et al. Towards a glaucoma risk index based on simulated hemodynamics from fundus images[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2018: 65-73.
4) Budai A, Bock R, Maier A, et al. Robust vessel segmentation in fundus images[J]. International journal of biomedical imaging, 2013, 2013.
5) Hemelings R, Elen B, Stalmans I, et al. Artery–vein segmentation in fundus images using a fully convolutional network[J]. Computerized Medical Imaging and Graphics, 2019, 76: 101636.
6) Zhou Y, Chen Z, Shen H, et al. A refined equilibrium generative adversarial network for retinal vessel segmentation[J]. Neurocomputing, 2021, 437: 118-130.
@inproceedings{zhou2021learning,
title={Learning to address intra-segment misclassification in retinal imaging},
author={Zhou, Yukun and Xu, Moucheng and Hu, Yipeng and Lin, Hongxiang and Jacob, Joseph and Keane, Pearse A and Alexander, Daniel C},
booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2021: 24th International Conference, Strasbourg, France, September 27--October 1, 2021, Proceedings, Part I 24},
pages={482--492},
year={2021},
organization={Springer}
}
@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}
}