Topology guaranteed segmentation of the human retina from OCT using convolutional neural networks

TL;DR

This paper introduces a deep learning approach with cascaded networks that guarantees topologically correct segmentation of retinal layers in OCT images, improving flexibility and efficiency over traditional graph-based methods.

Contribution

The authors develop a novel cascaded deep network framework that ensures topologically correct retinal layer segmentation without manual parameter tuning.

Findings

Achieved mean boundary error of 2.82 μm, comparable to state-of-the-art methods.

Provided a generalizable approach for layered structure segmentation.

Ensured layer topology through network design, even at the fovea.

Abstract

Optical coherence tomography (OCT) is a noninvasive imaging modality which can be used to obtain depth images of the retina. The changing layer thicknesses can thus be quantified by analyzing these OCT images, moreover these changes have been shown to correlate with disease progression in multiple sclerosis. Recent automated retinal layer segmentation tools use machine learning methods to perform pixel-wise labeling and graph methods to guarantee the layer hierarchy or topology. However, graph parameters like distance and smoothness constraints must be experimentally assigned by retinal region and pathology, thus degrading the flexibility and time efficiency of the whole framework. In this paper, we develop cascaded deep networks to provide a topologically correct segmentation of the retinal layers in a single feed forward propagation. The first network (S-Net) performs pixel-wise labeling and the second regression network (R-Net) takes the topologically unconstrained S-Net results and outputs layer thicknesses for each layer and each position. Relu activation is used as the final operation of the R-Net which guarantees non-negativity of the output layer thickness. Since the segmentation boundary position is acquired by summing up the corresponding non-negative layer thicknesses, the layer ordering (i.e., topology) of the reconstructed boundaries is guaranteed even at the fovea where the distances between boundaries can be zero. The R-Net is trained using simulated masks and thus can be generalized to provide topology guaranteed segmentation for other layered structures. This deep network has achieved comparable mean absolute boundary error (2.82 {\mu}m) to state-of-the-art graph methods (2.83 {\mu}m).