A New Method with SEU-Net model for Automatic Segmentation of Retinal Layers In Optical Coherence Tomography Images

Optical coherence tomography (OCT) images can reveal the ocular pathology of related diseases by analyzing the layer structure of the retina. Segmenting the retina is a standard procedure for ophthalmologists in diagnosing various diseases. Due to the large number of OCT images generated per patient, manual image analysis can be time-consuming and error-prone, thus compromising the efficiency of the diagnosis. Therefore, we propose an accurate retinal segmentation method combining SEU-Net (Squeeze and Excitation U-Net) model and graph search method. This method inserts SE blocks on the coding part of U-Net. The SE block improves the sensitivity of the network to the boundary information features of the retinal layer by recalculating features. This makes the less distinctive layers to be accurately segmented. By comparing the experimental results manually annotated by experts, the SEU-NET model combined with graph search method proposed in this paper can accurately segment the 9 retinal layer boundaries.