  Automatic segmentation of retinal blood vessels from fundus images plays an
important role in the computer aided diagnosis of retinal diseases. The task of
blood vessel segmentation is challenging due to the extreme variations in
morphology of the vessels against noisy background. In this paper, we formulate
the segmentation task as a multi-label inference task and utilize the implicit
advantages of the combination of convolutional neural networks and structured
prediction. Our proposed convolutional neural network based model achieves
strong performance and significantly outperforms the state-of-the-art for
automatic retinal blood vessel segmentation on DRIVE dataset with 95.33%
accuracy and 0.974 AUC score.
