End-to-end algorithm for the automatic detection of the neural canal opening in OCT images based on a multi-task deep learning model

Abstract

Neural canal opening (NCO) are important landmarks of the retinal pigment epithelium layer in the optic nerve head region. Conventional NCO detection employs multimodal measurements and feature engineering, which is usually suitable for one specific task. In this study, we proposed an end-to-end deep learning scenario for NCO detection based on single-modality features (OCT). The proposed method contains two visual tasks: one is to verify the existence of NCO points as a binary classification, and the other is to locate the NCO points as a coordinate regression. The feature representation of OCT images, extracted by a MobileNetV2 architecture, was evaluated under new testing data, with an average Euclidean distance error of 5.68 ± 4.45 pixels and an average intersection over union of 0.90 ± 0.03. This suggests that data-driven scenarios have the opportunity to provide a universal and efficient solution to various visual tasks from OCT images.