  In this paper, we present a novel neural network architecture for retinal
vessel segmentation that improves over the state of the art on two benchmark
datasets, is the first to run in real time on high resolution images, and its
small memory and processing requirements make it deployable in mobile and
embedded systems. The M2U-Net has a new encoder-decoder architecture that is
inspired by the U-Net. It adds pretrained components of MobileNetV2 in the
encoder part and novel contractive bottleneck blocks in the decoder part that,
combined with bilinear upsampling, drastically reduce the parameter count to
0.55M compared to 31.03M in the original U-Net. We have evaluated its
performance against a wide body of previously published results on three public
datasets. On two of them, the M2U-Net achieves new state-of-the-art performance
by a considerable margin. When implemented on a GPU, our method is the first to
achieve real-time inference speeds on high-resolution fundus images. We also
implemented our proposed network on an ARM-based embedded system where it
segments images in between 0.6 and 15 sec, depending on the resolution. Thus,
the M2U-Net enables a number of applications of retinal vessel structure
extraction, such as early diagnosis of eye diseases, retinal biometric
authentication systems, and robot assisted microsurgery.
