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We present a cascade deep neural network to segment retinal vessels involumetric optical coherence tomography (OCT). Two types of knowledge areinfused into the network for confining the searching regions. (1) Histology.The retinal vessels locate between the inner limiting membrane and the innernuclear layer of human retina. (2) Imaging. The red blood cells inside thevessels scatter the OCT probe light forward and form projection shadows on theretinal pigment epithelium (RPE) layer, which is avascular thus perfect forlocalizing the retinal vessel in transverse plane. Qualitative and quantitativecomparison results show that the proposed method outperforms thestate-of-the-art deep learning and graph-based methods. This work demonstrates,instead of modifying the architectures of the deep networks, incorporatingproper prior knowledge in the design of the image processing framework could bean efficient approach for handling such specific tasks.

Knowledge infused cascade convolutional neural network for segmenting retinal vessels in volumetric optical coherence tomography