eprintid: 10049640
rev_number: 42
eprint_status: archive
userid: 608
dir: disk0/10/04/96/40
datestamp: 2018-11-28 13:56:07
lastmod: 2021-09-25 23:30:32
status_changed: 2018-11-29 11:11:25
type: article
metadata_visibility: show
creators_name: Davidson, B
creators_name: Kalitzeos, A
creators_name: Carroll, J
creators_name: Dubra, A
creators_name: Ourselin, S
creators_name: Michaelides, M
creators_name: Bergeles, C
title: Automatic Cone Photoreceptor Localisation in Healthy and Stargardt Afflicted Retinas Using Deep Learning
ispublished: pub
subjects: MOOR
divisions: UCL
divisions: B02
divisions: C07
divisions: D08
divisions: B04
divisions: C05
divisions: F42
divisions: C06
divisions: F60
note: Copyright © The Author(s) 2018. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
abstract: We present a robust deep learning framework for the automatic localisation of cone photoreceptor cells in Adaptive Optics Scanning Light Ophthalmoscope (AOSLO) split-detection images. Monitoring cone photoreceptors with AOSLO imaging grants an excellent view into retinal structure and health, provides new perspectives into well known pathologies, and allows clinicians to monitor the effectiveness of experimental treatments. The MultiDimensional Recurrent Neural Network (MDRNN) approach developed in this paper is the first method capable of reliably and automatically identifying cones in both healthy retinas and retinas afflicted with Stargardt disease. Therefore, it represents a leap forward in the computational image processing of AOSLO images, and can provide clinical support in on-going longitudinal studies of disease progression and therapy. We validate our method using images from healthy subjects and subjects with the inherited retinal pathology Stargardt disease, which significantly alters image quality and cone density. We conduct a thorough comparison of our method with current state-of-the-art methods, and demonstrate that the proposed approach is both more accurate and appreciably faster in localizing cones. As further validation to the method’s robustness, we demonstrate it can be successfully applied to images of retinas with pathologies not present in the training data: achromatopsia, and retinitis pigmentosa.
date: 2018-05-21
date_type: published
publisher: NATURE PUBLISHING GROUP
official_url: https://doi.org/10.1038/s41598-018-26350-3
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1558385
doi: 10.1038/s41598-018-26350-3
lyricists_name: Bergeles, Christos
lyricists_name: Davidson, Benjamin
lyricists_name: Kalitzeos, Angelos
lyricists_name: Michaelides, Michel
lyricists_id: CBERG35
lyricists_id: BDAVI35
lyricists_id: AKALI23
lyricists_id: MMICH14
actors_name: Bracey, Alan
actors_id: ABBRA90
actors_role: owner
full_text_status: public
publication: Scientific Reports
volume: 8
article_number: 7911
pages: 13
issn: 2045-2322
citation:        Davidson, B;    Kalitzeos, A;    Carroll, J;    Dubra, A;    Ourselin, S;    Michaelides, M;    Bergeles, C;      (2018)    Automatic Cone Photoreceptor Localisation in Healthy and Stargardt Afflicted Retinas Using Deep Learning.                   Scientific Reports , 8     , Article 7911.  10.1038/s41598-018-26350-3 <https://doi.org/10.1038/s41598-018-26350-3>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10049640/1/s41598-018-26350-3.pdf