  Retinal vessel segmentation based on deep learning requires a lot of manual
labeled data. That is time-consuming, laborious and professional. What is
worse, the acquisition of abundant fundus images is difficult. These problems
are more serious due to the presence of abnormalities, varying size and shape
of the vessels, non-uniform illumination and anatomical changes. In this paper,
we propose a data-efficient semi-supervised learning framework, which
effectively combines the existing deep learning network with GAN and
self-training ideas. In view of the difficulty of tuning hyper-parameters of
semi-supervised learning, we propose a method for hyper-parameters selection
based on particle swarm optimization algorithm. To the best of our knowledge,
this work is the first demonstration that combines intelligent optimization
with semi-supervised learning for achieving the best performance. Under the
collaboration of adversarial learning, self-training and PSO to select optimal
hyper-parameters, we obtain the performance of retinal vessel segmentation
approximate to or even better than representative supervised learning using
only one tenth of the labeled data from DRIVE.
