  The precise detection of blood vessels in retinal images is crucial to the
early diagnosis of the retinal vascular diseases, e.g., diabetic, hypertensive
and solar retinopathies. Existing works often fail in predicting the abnormal
areas, e.g, sudden brighter and darker areas and are inclined to predict a
pixel to background due to the significant class imbalance, leading to high
accuracy and specificity while low sensitivity. To that end, we propose a novel
error attention refining network (ERA-Net) that is capable of learning and
predicting the potential false predictions in a two-stage manner for effective
retinal vessel segmentation. The proposed ERA-Net in the refine stage drives
the model to focus on and refine the segmentation errors produced in the
initial training stage. To achieve this, unlike most previous attention
approaches that run in an unsupervised manner, we introduce a novel error
attention mechanism which considers the differences between the ground truth
and the initial segmentation masks as the ground truth to supervise the
attention map learning. Experimental results demonstrate that our method
achieves state-of-the-art performance on two common retinal blood vessel
datasets.
