Context Encoder Network with Channel-Wise Attention Mechanism for Nerve Fibers Detection in Corneal Confocal Microscopy Images

Abstract

Diabetic peripheral neuropathy (DPN), one of the common long-term complications of diabetes, may affect the physical condition and quality of life of patients. Corneal confocal microscopy (CCM) is a rapid non-invasive ophthalmic imaging technique that can be used to observe the form of nerve fibers in sub-basal corneal nerve plexus directly. Analysis of the nerve fibers in CCM images quantifies features of nerve fibers, can apply to clinical diagnosis of DPN. This paper presents an attention deep learning model for detecting nerve fibers from CCM images, which combine context encoder network and squeeze-and-excitation networks. The algorithm with attention mechanism can solve the problem of the segmentation result is easily influenced by high level noise in CCM images and imbalance of nerve fiber pixels and background pixels to a certain degree. The proposed algorithm shows the best performance among common image segmentation deep learning model.