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 stateof-the-art for automatic retinal blood vessel segmentation on DRIVE dataset with 95.33% accuracy and 0.974 AUC score.
Inspired by this, recently, several methods have been proposed for retinal vessel segmentation in terms of deep learning. REF formulates the segmentation task as a multi-label inference problem and exploits the dependencies among neighboring pixels to improve the performance.
A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation
2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)
2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)
Computer Science