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

A generalizable approach based on U-Net model for automatic Intra retinal cyst segmentation in SD-OCT images