  In this paper, we propose an efficient blood vessel segmentation method for
the eye fundus images using adversarial learning with multiscale features and
kernel factorization. In the generator network of the adversarial framework,
spatial pyramid pooling, kernel factorization and squeeze excitation block are
employed to enhance the feature representation in spatial domain on different
scales with reduced computational complexity. In turn, the discriminator
network of the adversarial framework is formulated by combining convolutional
layers with an additional squeeze excitation block to differentiate the
generated segmentation mask from its respective ground truth. Before feeding
the images to the network, we pre-processed them by using edge sharpening and
Gaussian regularization to reach an optimized solution for vessel segmentation.
The output of the trained model is post-processed using morphological
operations to remove the small speckles of noise. The proposed method
qualitatively and quantitatively outperforms state-of-the-art vessel
segmentation methods using DRIVE and STARE datasets.
