A CNN based retinal regression model for Bruch’s Membrane Opening detection

摘要

Glaucoma is a common eye disease. It causes damage to patient’s vision and is difficult to diagnose. By locating Bruch’smembrane opening (BMO) in the Optical Coherence Tomography (OCT) image we can compute important diagnosticparameters which can increase the probability of early diagnosis of glaucoma. Besides the traditional methods, whichdepend on stratification results, this paper introduces a new method based on an end-to-end deep learning model todetect the BMO. Our model is composed of three parts. The first part is a CNN based retinal feature extraction network.It extracts feature map for both Optic Nerve Head (ONH) proposal and BMO detection. The second part is an ONHproposal network to detect region of interest (ROI) containing BMO. The third part is using the feature map from ONHproposal network to regress the location of BMO. The model has shown a clear precedence over other methods in termsof accuracy. Satisfactory results have been obtained when compared with clinical results.