Automatic Classification of Retinal Optical Coherence Tomography Images With Layer Guided Convolutional Neural Network Publisher



Huang L¹ ; He X¹ ; Fang L¹ ; Rabbani H² ; Chen X³ Show Affiliations
Source: IEEE Signal Processing Letters Published:2019

Abstract

Optical coherence tomography (OCT) enables instant and direct imaging of morphological retinal tissue and has become an essential imaging modality for ophthalmology diagnosis. As one of the important morphological retinal characteristics, the structural information of retinal layers provides meaningful diagnostic information and is closely related to several retinal diseases. In this letter, we propose a novel layer guided convolutional neural network (LGCNN) to identify normal retina and three common types of macular pathologies, namely, diabetic macular edema, drusen, and choroidal neovascularization. Specifically, an efficient segmentation network is first employed to generate the retinal layer segmentation maps, which can delineate two lesion-related retinal layers associated with the meaningful retinal lesions. Then, two well-designed subnetworks in LGCNN are utilized to integrate the information of two lesion-related layers. Consequently, LGCNN can efficiently focus on the meaningful lesion-related layer regions to improve OCT classification. The experimental results conducted on two clinically acquired datasets demonstrate the effectiveness of the proposed method. © 1994-2012 IEEE.