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      OCTA Multilayer and Multisector Peripapillary Microvascular Modeling for Diagnosing and Staging of Glaucoma

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          Abstract

          Purpose

          To develop and assess an automatic procedure for classifying and staging glaucomatous vascular damage based on optical coherence tomography angiography (OCTA) imaging.

          Methods

          OCTA scans (Zeiss Cirrus 5000 HD-OCT) from a random eye of 39 healthy subjects and 82 glaucoma patients were used to develop a new classification algorithm based on multilayer and multisector information. The averaged circumpapillary retinal nerve fiber layer (RNFL) thickness was also collected. Three models, support vector machine (SVM), random forest (RF), and gradient boosting (xGB), were developed and optimized for classifying between healthy and glaucoma patients, primary open-angle glaucoma (POAG) and normal-tension glaucoma (NTG), and glaucoma severity groups.

          Results

          All the models, the SVM (area under the receiver operating characteristic [AUROC] 0.89 ± 0.06), the RF (AUROC 0.86 ± 0.06), and the xGB (AUROC 0.85 ± 0.07), with 26, 22, and 29 vascular features obtained after feature selection, respectively, presented a similar performance to the RNFL thickness (AUROC 0.85 ± 0.06) in classifying healthy and glaucoma patients. The superficial vascular plexus was the most informative layer with the infero temporal sector as the most discriminative region of interest. No significant differentiation was obtained in discriminating the POAG from the NTG group. The xGB model, after feature selection, presented the best performance in classifying the severity groups (AUROC 0.76 ± 0.06), outperforming the RNFL (AUROC 0.67 ± 0.06).

          Conclusions

          OCTA multilayer and multisector information has similar performance to RNFL for glaucoma diagnosis, but it has an added value for glaucoma severity classification, showing promising results for staging glaucoma progression.

          Translational Relevance

          OCTA, in its current stage, has the potential to be used in clinical practice as a complementary imaging technique in glaucoma management.

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          Most cited references77

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          NIH Image to ImageJ: 25 years of image analysis

          For the past twenty five years the NIH family of imaging software, NIH Image and ImageJ have been pioneers as open tools for scientific image analysis. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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            Deep learning.

            Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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              Matplotlib: A 2D Graphics Environment

                Author and article information

                Journal
                Transl Vis Sci Technol
                Transl Vis Sci Technol
                tvst
                TVST
                Translational Vision Science & Technology
                The Association for Research in Vision and Ophthalmology
                2164-2591
                05 November 2020
                November 2020
                : 9
                : 2
                : 58
                Affiliations
                [1 ]Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
                [2 ]Research Group of Ophthalmology, Department of Neurosciences, KU Leuven, Leuven, Belgium
                [3 ]Cardiovascular R&D Center, Faculty of Medicine of the University of Porto, Porto, Portugal
                [4 ]Ophthalmology Department, Centro Hospitalar Sao João, Porto, Portugal
                [5 ]Delft University of Technology, Delft, The Netherlands
                [6 ]Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
                [7 ]Ophthalmology Department, University Hospitals Leuven, Leuven, Belgium
                Author notes
                Correspondence: Danilo Andrade De Jesus, Dr. Molewaterplein 40, Na 25.12, 3015 GD Rotterdam, The Netherlands. e-mail: d.andradedejesus@ 123456erasmusmc.nl
                [*]

                DADJ and LSB contributed equally to this work.

                Article
                TVST-19-1966
                10.1167/tvst.9.2.58
                7674004
                33224631
                2aae01eb-bb40-4736-87f2-9715d201c5d6
                Copyright 2020 The Authors

                This work is licensed under a Creative Commons Attribution 4.0 International License.

                History
                : 01 October 2020
                : 25 October 2019
                Categories
                Special Issue
                Special Issue

                oct angiography,microvascular density,glaucoma,machine learning

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