Dual-branch image projection network for geographic atrophy segmentation in retinal OCT images

Existing geographic atrophy (GA) segmentation tasks can only use 3D data, ignoring the fact that a large number of B-scan images contain lesion information. In this work, we proposed a multistage dual-branch image projection network (DIPN) to learn feature information in B-scan images to assist GA segmentation. Considering that segmenting 3D data slices using a 2D network architecture ignores the neighboring information between volume data slices, we introduced ConvLSTM. In addition, to make the network focus on the attention in the projection direction to capture the contextual relationships, we proposed the projection attention module. Meanwhile, considering that the current projection network uses a unidirectional pooling operation to achieve feature projection, multi-scale features and channel information are ignored in the projection process. Therefore, we proposed an adaptive pooling module that aims to adaptively reduce feature dimensions when grasping multi-scale features and channel information. Finally, to mitigate the effect of image contrast on the network segmentation performance, we proposed a contrastive learning enhancement module (CLE). To validate the effectiveness of our proposed method, we conducted experiments on two different datasets. The segmentation results show that our method is more effective than other methods in the GA segmentation task and the foveal avascular zone (FAZ) segmentation task.