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