FourierNet: shape-preserving network for henle’s fiber layer segmentation in optical coherence tomography images

Abstract

Henle's fiber layer (HFL), a retinal layer located in the outer retina between the outer nuclear and outer plexiform layers (ONL and OPL, respectively), is composed of uniformly linear photoreceptor axons and Müller cell processes. However, in the standard optical coherence tomography (OCT) imaging, this layer is usually included in the ONL since it is difficult to perceive HFL contours on OCT images. Due to its variable reflectivity under an imaging beam, delineating the HFL contours necessitates directional OCT, which requires additional imaging. This thesis addresses this issue by introducing a shape-preserving network, FourierNet, which achieves HFL segmentation in standard OCT scans with the target performance obtained when directional OCT is available. FourierNet is a new cascaded network design that puts forward the idea of benefiting the shape prior of the HFL in the network training. This design proposes to represent the shape prior by extracting Fourier descriptors on the HFL contours and defining an additional regression task of learning these descriptors. FourierNet then formulates HFL segmentation as concurrent learning of regression and classification tasks, in which Fourier descriptors are estimated from an input image to encode the shape prior and used together with the input image to construct the HFL segmentation map. Our experiments on 1470 images of 30 OCT scans reveal that quantifying the HFL shape with Fourier descriptors and concurrently learning them with the main task of segmentation leads to significantly better results. These findings indicate the effectiveness of designing a shape-preserving network to facilitate HFL segmentation by reducing the need to perform directional OCT imaging.