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In this paper, we present a novel neural network architecture for retinalvessel segmentation that improves over the state of the art on two benchmarkdatasets, is the first to run in real time on high resolution images, and itssmall memory and processing requirements make it deployable in mobile andembedded systems. The M2U-Net has a new encoder-decoder architecture that isinspired by the U-Net. It adds pretrained components of MobileNetV2 in theencoder part and novel contractive bottleneck blocks in the decoder part that,combined with bilinear upsampling, drastically reduce the parameter count to0.55M compared to 31.03M in the original U-Net. We have evaluated itsperformance against a wide body of previously published results on three publicdatasets. On two of them, the M2U-Net achieves new state-of-the-art performanceby a considerable margin. When implemented on a GPU, our method is the first toachieve real-time inference speeds on high-resolution fundus images. We alsoimplemented our proposed network on an ARM-based embedded system where itsegments images in between 0.6 and 15 sec, depending on the resolution. Thus,the M2U-Net enables a number of applications of retinal vessel structureextraction, such as early diagnosis of eye diseases, retinal biometricauthentication systems, and robot assisted microsurgery.

M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Resource-Constrained Environments