Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography.

Autor:	Venhuizen FG; Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, the Netherlands.; Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands., van Ginneken B; Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, the Netherlands., Liefers B; Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, the Netherlands.; Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands., van Asten F; Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands., Schreur V; Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands., Fauser S; Roche Pharma Research and Early Development, F. Hoffmann-La Roche Ltd, Basel, Switzerland.; Cologne University Eye Clinic, Cologne, Germany., Hoyng C; Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands., Theelen T; Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, the Netherlands.; Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands., Sánchez CI; Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, the Netherlands.; Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands.
Jazyk:	angličtina
Zdroj:	Biomedical optics express [Biomed Opt Express] 2018 Mar 07; Vol. 9 (4), pp. 1545-1569. Date of Electronic Publication: 2018 Mar 07 (Print Publication: 2018).
DOI:	10.1364/BOE.9.001545
Abstrakt:	We developed a deep learning algorithm for the automatic segmentation and quantification of intraretinal cystoid fluid (IRC) in spectral domain optical coherence tomography (SD-OCT) volumes independent of the device used for acquisition. A cascade of neural networks was introduced to include prior information on the retinal anatomy, boosting performance significantly. The proposed algorithm approached human performance reaching an overall Dice coefficient of 0.754 ± 0.136 and an intraclass correlation coefficient of 0.936, for the task of IRC segmentation and quantification, respectively. The proposed method allows for fast quantitative IRC volume measurements that can be used to improve patient care, reduce costs, and allow fast and reliable analysis in large population studies. Competing Interests: The authors declare that there are no conflicts of interest related to this article.
Databáze:	MEDLINE
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