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

Ruitao Xie, Jingxin Liu, Rui Cao, Connor S. Qiu, Jiang Duan*, Jon Garibaldi, Guoping Qiu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

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.

Original languageEnglish
Article number9193942
Pages (from-to)116-128
Number of pages13
JournalIEEE Transactions on Medical Imaging
Volume40
Issue number1
DOIs
Publication statusPublished - Jan 2021
Externally publishedYes

Keywords

  • Fovea localisation
  • coarse-to-fine framework
  • data augmentation
  • data fusion
  • deep learning
  • three-stage network

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