  Intra retinal fluids or Cysts are one of the important symptoms of macular
pathologies that are efficiently visualized in OCT images. Automatic
segmentation of these abnormalities has been widely investigated in medical
image processing studies. In this paper, we propose a new U-Net-based approach
for Intra retinal cyst segmentation across different vendors that improves some
of the challenges faced by previous deep-based techniques. The proposed method
has two main steps: 1- prior information embedding and input data adjustment,
and 2- IRC segmentation model. In the first step, we inject the information
into the network in a way that overcomes some of the network limitations in
receiving data and learning important contextual knowledge. And in the next
step, we introduced a connection module between encoder and decoder parts of
the standard U-Net architecture that transfers information more effectively
from the encoder to the decoder part. Two public datasets namely OPTIMA and
KERMANY were employed to evaluate the proposed method. Results showed that the
proposed method is an efficient vendor-independent approach for IRC
segmentation with mean Dice values of 0.78 and 0.81 on the OPTIMA and KERMANY
datasets, respectively.
