  State-of-the-art methods for retinal vessel segmentation mainly rely on
manually labeled vessels as the ground truth for supervised training. The
quality of manual labels plays an essential role in the segmentation accuracy,
while in practice it could vary a lot and in turn could substantially mislead
the training process and limit the segmentation accuracy. This paper aims to
"purify" any comprehensive training set, which consists of data annotated by
various observers, via refining low-quality manual labels in the dataset. To
this end, we have developed a novel label refinement method based on an
iterative generative adversarial network (GAN). Our iterative GAN is trained
based on a set of high-quality patches (i.e. with consistent manual labels
among different observers) and low-quality patches with noisy manual vessel
labels. A simple yet effective method has been designed to simulate low-quality
patches with noises which conform to the distribution of real noises from human
observers. To evaluate the effectiveness of our method, we have trained four
state-of-the-art retinal vessel segmentation models using the purified dataset
obtained from our method and compared their performance with those trained
based on the original noisy datasets. Experimental results on two datasets
DRIVE and CHASE_DB1 demonstrate that obvious accuracy improvements can be
achieved for all the four models when using the purified datasets from our
method.
