A generalizable approach based on the U‐Net model for automatic intraretinal cyst segmentation in SD‐OCT images

Abstract

In this article, we propose a new U‐Net‐based approach for intraretinal cyst segmentation across different vendors that improve 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, (2) the 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 introduce a connection module between the encoder and decoder parts that transfers information more effectively from the encoder to the decoder. Two public datasets, namely, OPTIMA and KERMANY, are employed to evaluate the proposed method. The results show that the proposed method is an efficient vendor‐independent approach for the segmentation of intraretinal cystoid fluid with mean Dice values of 0.78 and 0.81 on the OPTIMA and KERMANY datasets, respectively.

Deep learning based characterization of human organoids using optical coherence tomography

“…The local information is prone to fade, and global information is prone to be highlighted in the terminal layers of the proposed model due to a decrease of spatial resolution. Inspired by atrous pyramid pooling [ 38 , 39 ], we enriched the global feature maps in the gateway layer by proposing a multi-kernel receptive filed module consisting of 4 dilated convolutions with dilation rates r = 2, 3, 5, 7 as ( Eq. (5) ) Where x , w , and y indicated the input feature map, convolution filter, and output feature map, respectively.…”