  Vessel segmentation in fundus is a key diagnostic capability in
ophthalmology, and there are various challenges remained in this essential
task. Early approaches indicate that it is often difficult to obtain desirable
segmentation performance on thin vessels and boundary areas due to the
imbalance of vessel pixels with different thickness levels. In this paper, we
propose a novel two-stream Meticulous-Processing Network (MP-Net) for tackling
this problem. To pay more attention to the thin vessels and boundary areas, we
firstly propose an efficient hierarchical model automatically stratifies the
ground-truth masks into different thickness levels. Then a novel two-stream
adversarial network is introduced to use the stratification results with a
balanced loss function and an integration operation to achieve a better
performance, especially in thin vessels and boundary areas detecting. Our model
is proved to outperform state-of-the-art methods on DRIVE, STARE, and CHASE_DB1
datasets.
