End-to-End Fovea Localisation in Colour Fundus Images with a Hierarchical Deep Regression Network

Ruitao Xie

Abstract:

Accurately locating the fovea is a prerequisite for
developing computer aided diagnosis (CAD) of retinal diseases.
In colour fundus images of the retina, the fovea is a fuzzy region
lacking prominent visual features and this makes it difficult
to directly locate the fovea. While traditional methods rely
on explicitly extracting image features from the surrounding
structures such as the optic disc and various vessels to infer
the position of the fovea, deep learning based regression technique can implicitly model the relation between the fovea and
other nearby anatomical structures to determine the location of
the fovea in an end-to-end fashion. Although promising, using
deep learning for fovea localisation also has many unsolved
challenges. In this paper, we present a new end-to-end fovea
localisation method based on a hierarchical coarse-to-fine deep
regression neural network. The innovative features of the new
method include a multi-scale feature fusion technique and a self-attention technique to exploit location, semantic, and contextual
information in an integrated framework, a multi-field-of-view
(multi-FOV) feature fusion technique for context-aware feature
learning and a Gaussian-shift-cropping method for augmenting
effective training data. We present extensive experimental results
on two public databases and show that our new method achieved
state-of-the-art performances. We also present a comprehensive
ablation study and analysis to demonstrate the technical soundness and effectiveness of the overall framework and its various
constituent components.