  A variety of deep neural networks have been applied in medical image
segmentation and achieve good performance. Unlike natural images, medical
images of the same imaging modality are characterized by the same pattern,
which indicates that same normal organs or tissues locate at similar positions
in the images. Thus, in this paper we try to incorporate the prior knowledge of
medical images into the structure of neural networks such that the prior
knowledge can be utilized for accurate segmentation. Based on this idea, we
propose a novel deep network called knowledge-based fully convolutional network
(KFCN) for medical image segmentation. The segmentation function and
corresponding error is analyzed. We show the existence of an asymptotically
stable region for KFCN which traditional FCN doesn't possess. Experiments
validate our knowledge assumption about the incorporation of prior knowledge
into the convolution kernels of KFCN and show that KFCN can achieve a
reasonable segmentation and a satisfactory accuracy.
