Fully-automated atrophy segmentation in dry age-related macular degeneration in optical coherence tomography.
Sci Rep
; 11(1): 21893, 2021 11 08.
Article
em En
| MEDLINE
| ID: mdl-34751189
ABSTRACT
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Tomografia de Coerência Óptica
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Atrofia Geográfica
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Degeneração Macular
Tipo de estudo:
Evaluation_studies
/
Prognostic_studies
Limite:
Aged
/
Aged80
/
Female
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Humans
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Male
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Suíça