Deep learning algorithms to isolate and quantify the structures of the anterior segment in optical coherence tomography images.
Anterior chamber
Sclera and Episclera
Journal
The British journal of ophthalmology
ISSN: 1468-2079
Titre abrégé: Br J Ophthalmol
Pays: England
ID NLM: 0421041
Informations de publication
Date de publication:
09 2021
09 2021
Historique:
received:
15
12
2019
accepted:
03
08
2020
revised:
14
04
2020
pubmed:
28
9
2020
medline:
29
9
2021
entrez:
27
9
2020
Statut:
ppublish
Résumé
Accurate isolation and quantification of intraocular dimensions in the anterior segment (AS) of the eye using optical coherence tomography (OCT) images is important in the diagnosis and treatment of many eye diseases, especially angle-closure glaucoma. In this study, we developed a deep convolutional neural network (DCNN) for the localisation of the scleral spur; moreover, we introduced an information-rich segmentation approach for this localisation problem. An ensemble of DCNNs for the segmentation of AS structures (iris, corneosclera shell adn anterior chamber) was developed. Based on the results of two previous processes, an algorithm to automatically quantify clinically important measurements were created. 200 images from 58 patients (100 eyes) were used for testing. With limited training data, the DCNN was able to detect the scleral spur on unseen anterior segment optical coherence tomography (ASOCT) images as accurately as an experienced ophthalmologist on the given test dataset and simultaneously isolated the AS structures with a Dice coefficient of 95.7%. We then automatically extracted eight clinically relevant ASOCT measurements and proposed an automated quality check process that asserts the reliability of these measurements. When combined with an OCT machine capable of imaging multiple radial sections, the algorithms can provide a more complete objective assessment. The total segmentation and measurement time for a single scan is less than 2 s. This is an essential step towards providing a robust automated framework for reliable quantification of ASOCT scans, for applications in the diagnosis and management of angle-closure glaucoma.
Sections du résumé
BACKGROUND/AIMS
Accurate isolation and quantification of intraocular dimensions in the anterior segment (AS) of the eye using optical coherence tomography (OCT) images is important in the diagnosis and treatment of many eye diseases, especially angle-closure glaucoma.
METHOD
In this study, we developed a deep convolutional neural network (DCNN) for the localisation of the scleral spur; moreover, we introduced an information-rich segmentation approach for this localisation problem. An ensemble of DCNNs for the segmentation of AS structures (iris, corneosclera shell adn anterior chamber) was developed. Based on the results of two previous processes, an algorithm to automatically quantify clinically important measurements were created. 200 images from 58 patients (100 eyes) were used for testing.
RESULTS
With limited training data, the DCNN was able to detect the scleral spur on unseen anterior segment optical coherence tomography (ASOCT) images as accurately as an experienced ophthalmologist on the given test dataset and simultaneously isolated the AS structures with a Dice coefficient of 95.7%. We then automatically extracted eight clinically relevant ASOCT measurements and proposed an automated quality check process that asserts the reliability of these measurements. When combined with an OCT machine capable of imaging multiple radial sections, the algorithms can provide a more complete objective assessment. The total segmentation and measurement time for a single scan is less than 2 s.
CONCLUSION
This is an essential step towards providing a robust automated framework for reliable quantification of ASOCT scans, for applications in the diagnosis and management of angle-closure glaucoma.
Identifiants
pubmed: 32980820
pii: bjophthalmol-2019-315723
doi: 10.1136/bjophthalmol-2019-315723
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1231-1237Informations de copyright
© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.
Déclaration de conflit d'intérêts
Competing interests: The preprint version of this manuscript can be found at arXiv:1909.00331. MJAG and AHT are co-founders of Abyss Processing.