Show simple item record

dc.contributor.authorMuller, J
dc.contributor.authorAlonso-Caneiro, D
dc.contributor.authorRead, SA
dc.contributor.authorVincent, SJ
dc.contributor.authorCollins, MJ
dc.date.accessioned2022-03-08T00:59:30Z
dc.date.available2022-03-08T00:59:30Z
dc.date.issued2022
dc.identifier.issn2164-2591
dc.identifier.doi10.1167/tvst.11.2.23
dc.identifier.urihttp://hdl.handle.net/10072/412970
dc.description.abstractPurpose: The purpose of this study was to develop a deep learning model for automatic binarization of the choroidal tissue, separating choroidal blood vessels from nonvascu-lar stromal tissue, in optical coherence tomography (OCT) images from healthy young subjects. Methods: OCT images from an observational longitudinal study of 100 children were used for training, validation, and testing of 5 fully semantic networks, which provided a binarized output of the choroid. These outputs were compared with ground truth images, generated from a local binarization technique after manually optimizing the analysis window size for each individual image. The performance was evaluated using accuracy and repeatability metrics. The methods were also compared with a fixed window size local binarization technique, which has been commonly used previously. Results: The tested deep learning methods provided a good performance in terms of accuracy and repeatability. With the U-Net and SegNet networks showing >96% accuracy. All methods displayed a high level of repeatability relative to the ground truth. For analysis of the choroidal vascularity index (a commonly used metric derived from the binarized image), SegNet showed the closest agreement with the ground truth and high repeatability. The fixed window size showed a reduced accuracy compared to other methods. Conclusions: Fully semantic networks such as U-Net and SegNet displayed excellent performance for the binarization task. These methods provide a useful approach for clinical and research applications of deep learning tools for the binarization of the choroid in OCT images. Translational Relevance: Deep learning models provide a novel, robust solution to automatically binarize the choroidal tissue in OCT images.
dc.description.peerreviewedYes
dc.languageeng
dc.publisherAssociation for Research in Vision and Ophthalmology (ARVO)
dc.relation.ispartofpagefrom23
dc.relation.ispartofissue2
dc.relation.ispartofjournalTranslational Vision Science and Technology
dc.relation.ispartofvolume11
dc.subject.fieldofresearchOphthalmology and optometry
dc.subject.fieldofresearchBiomedical engineering
dc.subject.fieldofresearchcode3212
dc.subject.fieldofresearchcode4003
dc.titleApplication of Deep Learning Methods for Binarization of the Choroid in Optical Coherence Tomography Images
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationMuller, J; Alonso-Caneiro, D; Read, SA; Vincent, SJ; Collins, MJ, Application of Deep Learning Methods for Binarization of the Choroid in Optical Coherence Tomography Images, Translational Vision Science and Technology, 2022, 11 (2), pp. 23-
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/
dc.date.updated2022-03-08T00:31:55Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© The Author(s) 2022. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorAlonso-Caneiro, David


Files in this item

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record