  The accurate retinal vessel segmentation (RVS) is of great significance to
assist doctors in the diagnosis of ophthalmology diseases and other systemic
diseases. Manually designing a valid neural network architecture for retinal
vessel segmentation requires high expertise and a large workload. In order to
improve the performance of vessel segmentation and reduce the workload of
manually designing neural network, we propose novel approach which applies
neural architecture search (NAS) to optimize an encoder-decoder architecture
for retinal vessel segmentation. A modified evolutionary algorithm is used to
evolve the architectures of encoder-decoder framework with limited computing
resources. The evolved model obtained by the proposed approach achieves top
performance among all compared methods on the three datasets, namely DRIVE,
STARE and CHASE_DB1, but with much fewer parameters. Moreover, the results of
cross-training show that the evolved model is with considerable scalability,
which indicates a great potential for clinical disease diagnosis.
