Fully-automated atrophy segmentation in dry age-related macular degeneration in optical coherence tomography.

Détails

ID Serval
serval:BIB_4CA55724AC15
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Fully-automated atrophy segmentation in dry age-related macular degeneration in optical coherence tomography.
Périodique
Scientific reports
Auteur⸱e⸱s
Derradji Y., Mosinska A., Apostolopoulos S., Ciller C., De Zanet S., Mantel I.
ISSN
2045-2322 (Electronic)
ISSN-L
2045-2322
Statut éditorial
Publié
Date de publication
08/11/2021
Peer-reviewed
Oui
Volume
11
Numéro
1
Pages
21893
Langue
anglais
Notes
Publication types: Evaluation Study ; Journal Article
Publication Status: epublish
Résumé
Age-related macular degeneration (AMD) is a progressive retinal disease, causing vision loss. A more detailed characterization of its atrophic form became possible thanks to the introduction of Optical Coherence Tomography (OCT). However, manual atrophy quantification in 3D retinal scans is a tedious task and prevents taking full advantage of the accurate retina depiction. In this study we developed a fully automated algorithm segmenting Retinal Pigment Epithelial and Outer Retinal Atrophy (RORA) in dry AMD on macular OCT. 62 SD-OCT scans from eyes with atrophic AMD (57 patients) were collected and split into train and test sets. The training set was used to develop a Convolutional Neural Network (CNN). The performance of the algorithm was established by cross validation and comparison to the test set with ground-truth annotated by two graders. Additionally, the effect of using retinal layer segmentation during training was investigated. The algorithm achieved mean Dice scores of 0.881 and 0.844, sensitivity of 0.850 and 0.915 and precision of 0.928 and 0.799 in comparison with Expert 1 and Expert 2, respectively. Using retinal layer segmentation improved the model performance. The proposed model identified RORA with performance matching human experts. It has a potential to rapidly identify atrophy with high consistency.
Mots-clé
Aged, Aged, 80 and over, Algorithms, Deep Learning, Female, Geographic Atrophy/diagnostic imaging, Humans, Macular Degeneration/diagnostic imaging, Male, Neural Networks, Computer, Observer Variation, Pattern Recognition, Automated/methods, Pattern Recognition, Automated/statistics & numerical data, Retinal Pigment Epithelium/diagnostic imaging, Tomography, Optical Coherence/methods, Tomography, Optical Coherence/statistics & numerical data
Pubmed
Web of science
Open Access
Oui
Création de la notice
15/11/2021 16:24
Dernière modification de la notice
02/03/2022 7:36
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