Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks
Published in
arXiv.org(2017)External Links:
TL
TL;DR
This paper presents a method that generates the precise map of retinal vessels using generative adversarial training and achieves dice coefficient of 0.829 on DRIVE dataset and0.834 on STARE dataset which is the state-of-the-art performance on both datasets.
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.
References17 items
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Image-to-Image Translation with Conditional Adversarial Networks
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DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field
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Deep Retinal Image Understanding
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Deep Residual Learning for Image Recognition
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