Cascade Dual-branch Deep Neural Networks for Retinal Layer and fluid Segmentation of Optical Coherence Tomography Incorporating Spatial Prior

Da Ma, Donghuan Lu, Morgan Heisler, Setareh Dabiri, Sieun Lee, Gavin Weiguan Ding, Marinko V. Sarunic, Mirza Faisal Beg

Keywords: retinal layer segmentation, optical coherence tomography, fully convolutional network, relative positional map

TL;DR: Retinal Layer and Fluid Segmentation with Relative Positional Map

Abstract: Optical coherence tomography (OCT) is a non-invasive imaging technology that can provide micrometer-resolution cross-sectional images of the inner structures of the eye. It is widely used for the diagnosis of ophthalmic diseases with retinal alteration such as layer deformation and fluid accumulation. In this paper, a novel framework was proposed to segment retinal layers with fluid presence. The main contribution of this study is two folds: 1) we developed a cascaded network framework to incorporate the prior structural knowledge; 2) we proposed a novel two-path deep neural network which includes both the U-Net architecture as well as the original implementation of the fully convolutional network, concatenated into a final multi-level dilated layer to achieve accurate simultaneous layer and fluid segmentation. Cross validation experiments proved that the proposed network has superior performance comparing with the state-of-the-art methods by up to 3%, and incorporating the relative positional map structural prior information could further improve the performance (up to 1%) regardless of the network.

Track: full conference paper

Paper Type: both

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