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

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


Ruitao Xie

Jingxin Liu

Rui Cao

Connor S. Qiu

Jiang Duan

Professor of Visual Information Processing


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-shiftcropping 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.


Xie, R., Liu, J., Cao, R., Qiu, C. S., Duan, J., Garibaldi, J., & Qiu, G. (2020). End-to-End Fovea Localisation in Colour Fundus Images with a Hierarchical Deep Regression Network. IEEE Transactions on Medical Imaging,

Journal Article Type Article
Acceptance Date Sep 4, 2020
Online Publication Date Sep 10, 2020
Publication Date Sep 10, 2020
Deposit Date Sep 18, 2020
Publicly Available Date Sep 18, 2020
Journal IEEE Transactions on Medical Imaging
Print ISSN 0278-0062
Electronic ISSN 1558-254X
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Keywords Electrical and Electronic Engineering; Radiological and Ultrasound Technology; Software; Computer Science Applications
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