  Retinal artery/vein (A/V) classification plays a critical role in the
clinical biomarker study of how various systemic and cardiovascular diseases
affect the retinal vessels. Conventional methods of automated A/V
classification are generally complicated and heavily depend on the accurate
vessel segmentation. In this paper, we propose a multi-task deep neural network
with spatial activation mechanism that is able to segment full retinal vessel,
artery and vein simultaneously, without the pre-requirement of vessel
segmentation. The input module of the network integrates the domain knowledge
of widely used retinal preprocessing and vessel enhancement techniques. We
specially customize the output block of the network with a spatial activation
mechanism, which takes advantage of a relatively easier task of vessel
segmentation and exploits it to boost the performance of A/V classification. In
addition, deep supervision is introduced to the network to assist the low level
layers to extract more semantic information. The proposed network achieves
pixel-wise accuracy of 95.70% for vessel segmentation, and A/V classification
accuracy of 94.50%, which is the state-of-the-art performance for both tasks on
the AV-DRIVE dataset. Furthermore, we have also tested the model performance on
INSPIRE-AVR dataset, which achieves a skeletal A/V classification accuracy of
91.6%.
