A Corneal Nerve Segmentation Algorithm Based on Improved ResU-Net

The automatic segmentation of corneal nerve images is crucial to the diagnosis and screening of several diseases such as diabetic neuropathy but it suffers from the low segmentation efficiency caused by the low contrast of corneal nerve images and the existence of non-neural structures. To address the problem this paper proposes a novel corneal nerve segmentation algorithm based on attention mechanism which introduces multi-scale residual module attention mechanism module multi-scale image input module and multi-layer loss function output module into the ResU-Net structure.The multiscale residual module is used to add multi-scale representation information into the residual module to improve the multi-scale feature extraction ability of the convolutional layer.The attention mechanism module consisting of channel and spatial attentions uses the network to optimize the weight of the target features in the encoder and decoder so as to enhance the features of the target area as well as suppress the features of background and noise area. Furthermore the multi-scale image input and multi-layer function output modules are added to supervise the feature learning of each network layer.Experimental results show that the proposed method outperforms the existing mainstream segmentation algorithms with its Area Under Curve(AUC)reaching 0.990 and its Sencificity(Sen)reaching 0.880.