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

• URL: http://arxiv.org/abs/2202.00465v1
• Date: Tue, 1 Feb 2022 15:23:00 GMT
• Title: A generalizable approach based on U-Net model for automatic Intra retinal cyst segmentation in SD-OCT images
• Authors: Razieh Ganjee, Mohsen Ebrahimi Moghaddam, Ramin Nourinia
• Abstract summary: We propose a new U-Net-based approach for Intra retinal cyst segmentation across different vendors. 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 in the next step, we introduced a connection module between encoder and decoder parts of the standard U-Net architecture.
• Score: 1.6114012813668928
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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