Embedded Residual Recurrent Network and Graph Search for the Segmentation of Retinal Layer Boundaries in Optical Coherence Tomography

For the study of various retinal diseases, an accurate quantitative analysis of the retinal layer is essential for assessing the severity of the disease and diagnosing the progression of the disease. Optical coherence tomography (OCT) images can clearly show each layer of the retinal structure and detect subtle early lesions, thus providing a gold standard for the diagnosis of retinal diseases. In this article, we propose a coarse-to-fine retinal layer boundary segmentation method based on the embedded residual recurrent network (ERR-Net) and the graph search (GS). Considering the integrity of information transmission and the dependence of features, we first design a novel end-to-end residual recurrent network to roughly segment the retinal layer boundaries. The proposed ERR-Net not only solves the gradient problem brought by the depth but also fully captures the global spatial structure of the image. Second, we employ a GS algorithm for boundary continuous to make the layer boundary segmentation results more accurate. Finally, we evaluate the effectiveness of the proposed method on three publicly available datasets and compare it with the state-of-the-art methods on each dataset. The quantitative results and visual effects show that the proposed method outperforms the state-of-the-art approaches in the segmentation of retinal layer boundaries in OCT images. Moreover, the results also show that the proposed method has good algorithm stability on three datasets with different sizes, characteristics, and segmentation difficulties.