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Open AccessArticle

Analyzing Age-Related Macular Degeneration Progression in Patients with Geographic Atrophy Using Joint Autoencoders for Unsupervised Change Detection

1
ISEP, DaSSIP Team, 92130 Issy-Les-Moulineaux, France
2
Université Paris 13, LIPN - CNRS UMR 7030, 93430 Villetaneuse, France
3
Clinical Imaging Center 1423, Quinze-Vingts Hospital, INSERM-DGOS Clinical Investigation Center, 75012 Paris, France
*
Authors to whom correspondence should be addressed.
J. Imaging 2020, 6(7), 57; https://doi.org/10.3390/jimaging6070057
Received: 6 May 2020 / Revised: 20 June 2020 / Accepted: 23 June 2020 / Published: 29 June 2020
(This article belongs to the Special Issue Deep Learning on Medical Image Analysis)
Age-Related Macular Degeneration (ARMD) is a progressive eye disease that slowly causes patients to go blind. For several years now, it has been an important research field to try to understand how the disease progresses and find effective medical treatments. Researchers have been mostly interested in studying the evolution of the lesions using different techniques ranging from manual annotation to mathematical models of the disease. However, artificial intelligence for ARMD image analysis has become one of the main research focuses to study the progression of the disease, as accurate manual annotation of its evolution has proved difficult using traditional methods even for experienced practicians. In this paper, we propose a deep learning architecture that can detect changes in the eye fundus images and assess the progression of the disease. Our method is based on joint autoencoders and is fully unsupervised. Our algorithm has been applied to pairs of images from different eye fundus images time series of 24 ARMD patients. Our method has been shown to be quite effective when compared with other methods from the literature, including non-neural network based algorithms that still are the current standard to follow the disease progression and change detection methods from other fields. View Full-Text
Keywords: ARMD; change detection; unsupervised learning; medical imaging ARMD; change detection; unsupervised learning; medical imaging
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MDPI and ACS Style

Dupont, G.; Kalinicheva, E.; Sublime, J.; Rossant, F.; Pâques, M. Analyzing Age-Related Macular Degeneration Progression in Patients with Geographic Atrophy Using Joint Autoencoders for Unsupervised Change Detection. J. Imaging 2020, 6, 57. https://doi.org/10.3390/jimaging6070057

AMA Style

Dupont G, Kalinicheva E, Sublime J, Rossant F, Pâques M. Analyzing Age-Related Macular Degeneration Progression in Patients with Geographic Atrophy Using Joint Autoencoders for Unsupervised Change Detection. Journal of Imaging. 2020; 6(7):57. https://doi.org/10.3390/jimaging6070057

Chicago/Turabian Style

Dupont, Guillaume; Kalinicheva, Ekaterina; Sublime, Jérémie; Rossant, Florence; Pâques, Michel. 2020. "Analyzing Age-Related Macular Degeneration Progression in Patients with Geographic Atrophy Using Joint Autoencoders for Unsupervised Change Detection" J. Imaging 6, no. 7: 57. https://doi.org/10.3390/jimaging6070057

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