  Accurate multi-class segmentation is a long-standing challenge in medical
imaging, especially in scenarios where classes share strong similarity.
Segmenting retinal blood vessels in retinal photographs is one such scenario,
in which arteries and veins need to be identified and differentiated from each
other and from the background. Intra-segment misclassification, i.e. veins
classified as arteries or vice versa, frequently occurs when arteries and veins
intersect, whereas in binary retinal vessel segmentation, error rates are much
lower. We thus propose a new approach that decomposes multi-class segmentation
into multiple binary, followed by a binary-to-multi-class fusion network. The
network merges representations of artery, vein, and multi-class feature maps,
each of which are supervised by expert vessel annotation in adversarial
training. A skip-connection based merging process explicitly maintains
class-specific gradients to avoid gradient vanishing in deep layers, to favor
the discriminative features. The results show that, our model respectively
improves F1-score by 4.4\%, 5.1\%, and 4.2\% compared with three
state-of-the-art deep learning based methods on DRIVE-AV, LES-AV, and HRF-AV
data sets.
