Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks

Jaemin Son, Sang Jun Park, Kyu-Hwan Jung
6/28/2017
cs.CVcs.LG

No official code found. Best implementation: ChengBinJin/V-GAN-tensorflow (232 stars). 2 more below.

Abstract

Retinal vessel segmentation is an indispensable step for automatic detection of retinal diseases with fundoscopic images. Though many approaches have been proposed, existing methods tend to miss fine vessels or allow false positives at terminal branches. Let alone under-segmentation, over-segmentation is also problematic when quantitative studies need to measure the precise width of vessels. In this paper, we present a method that generates the precise map of retinal vessels using generative adversarial training. Our methods achieve dice coefficient of 0.829 on DRIVE dataset and 0.834 on STARE dataset which is the state-of-the-art performance on both datasets.

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Code Implementations(3)

ChengBinJin/V-GAN-tensorflowCommunity25%

A tensorflow implementation of "Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks"

23249
guyuchao/Vessel-wgan-pytorchCommunity25%

An implementation of《Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks》

8920
bukhariaujih/v_ganCommunity25%

Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks

31

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Cite this paper

@article{son2017retinal,
  title  = {Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks},
  author = {Jaemin Son and Sang Jun Park and Kyu-Hwan Jung},
  year   = {2017},
  eprint = {1706.09318},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1706.09318}
}

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