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In this paper, we propose an efficient blood vessel segmentation method forthe eye fundus images using adversarial learning with multiscale features andkernel factorization. In the generator network of the adversarial framework,spatial pyramid pooling, kernel factorization and squeeze excitation block areemployed to enhance the feature representation in spatial domain on differentscales with reduced computational complexity. In turn, the discriminatornetwork of the adversarial framework is formulated by combining convolutionallayers with an additional squeeze excitation block to differentiate thegenerated segmentation mask from its respective ground truth. Before feedingthe images to the network, we pre-processed them by using edge sharpening andGaussian regularization to reach an optimized solution for vessel segmentation.The output of the trained model is post-processed using morphologicaloperations to remove the small speckles of noise. The proposed methodqualitatively and quantitatively outperforms state-of-the-art vesselsegmentation methods using DRIVE and STARE datasets.

Adversarial Learning with Multiscale Features and Kernel Factorization for Retinal Blood Vessel Segmentation