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Classification of Pachychoroid on Optical Coherence Tomographic En Face Images Using Deep Convolutional Neural Networks
PURPOSE: To study the efficacy of deep convolutional neural networks (DCNNs) to differentiate pachychoroid from nonpachychoroid on en face optical coherence tomography (OCT) images at the large choroidal vessel. METHODS: En face OCT images were collected from eyes with neovascular age-related macula...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Association for Research in Vision and Ophthalmology
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8255502/ https://www.ncbi.nlm.nih.gov/pubmed/34185057 http://dx.doi.org/10.1167/tvst.10.7.28 |
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author | Lee, Kook Ra, Ho Lee, Jun Hyuk Baek, Jiwon Lee, Won Ki |
author_facet | Lee, Kook Ra, Ho Lee, Jun Hyuk Baek, Jiwon Lee, Won Ki |
author_sort | Lee, Kook |
collection | PubMed |
description | PURPOSE: To study the efficacy of deep convolutional neural networks (DCNNs) to differentiate pachychoroid from nonpachychoroid on en face optical coherence tomography (OCT) images at the large choroidal vessel. METHODS: En face OCT images were collected from eyes with neovascular age-related macular degeneration, polypoidal choroidal vasculopathy, and central serous chorioretinopathy. All images were prelabeled pachychoroid or nonpachychoroid based on quantitative and qualitative criteria for choroidal morphology on multimodal imaging by two retina specialists. In total, 1188 nonpachychoroid and 884 pachychoroid images were used for training (80%) and validation (20%). Accuracy for identification of pachychoroid by DCNN models was analyzed. Trained models were tested on a test set containing 79 nonpachychoroid and 93 pachychoroid images. RESULTS: The accuracy on the validation set was 94.1%, 93.2%, 94.7%, and 94.4% in DenseNet, GoogLeNet, ResNet50, and Inception-v3, respectively. On a test set, each model demonstrated accuracy of 80.2%, 83.1%, 89.5%, and 90.1% and an F1 score of 0.782, 0.824, 0.904, and 0.901, respectively. CONCLUSIONS: DCNN models could classify pachychoroid and nonpachychoroid with good performance on OCT en face images. Automated classification of pachychoroid will be useful for tailored treatment of individual patients with exudative maculopathy. TRANSLATIONAL RELEVANCE: En face OCT images can be used by DCNN for classification of pachychoroid. |
format | Online Article Text |
id | pubmed-8255502 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Association for Research in Vision and Ophthalmology |
record_format | MEDLINE/PubMed |
spelling | pubmed-82555022021-07-09 Classification of Pachychoroid on Optical Coherence Tomographic En Face Images Using Deep Convolutional Neural Networks Lee, Kook Ra, Ho Lee, Jun Hyuk Baek, Jiwon Lee, Won Ki Transl Vis Sci Technol Article PURPOSE: To study the efficacy of deep convolutional neural networks (DCNNs) to differentiate pachychoroid from nonpachychoroid on en face optical coherence tomography (OCT) images at the large choroidal vessel. METHODS: En face OCT images were collected from eyes with neovascular age-related macular degeneration, polypoidal choroidal vasculopathy, and central serous chorioretinopathy. All images were prelabeled pachychoroid or nonpachychoroid based on quantitative and qualitative criteria for choroidal morphology on multimodal imaging by two retina specialists. In total, 1188 nonpachychoroid and 884 pachychoroid images were used for training (80%) and validation (20%). Accuracy for identification of pachychoroid by DCNN models was analyzed. Trained models were tested on a test set containing 79 nonpachychoroid and 93 pachychoroid images. RESULTS: The accuracy on the validation set was 94.1%, 93.2%, 94.7%, and 94.4% in DenseNet, GoogLeNet, ResNet50, and Inception-v3, respectively. On a test set, each model demonstrated accuracy of 80.2%, 83.1%, 89.5%, and 90.1% and an F1 score of 0.782, 0.824, 0.904, and 0.901, respectively. CONCLUSIONS: DCNN models could classify pachychoroid and nonpachychoroid with good performance on OCT en face images. Automated classification of pachychoroid will be useful for tailored treatment of individual patients with exudative maculopathy. TRANSLATIONAL RELEVANCE: En face OCT images can be used by DCNN for classification of pachychoroid. The Association for Research in Vision and Ophthalmology 2021-06-29 /pmc/articles/PMC8255502/ /pubmed/34185057 http://dx.doi.org/10.1167/tvst.10.7.28 Text en Copyright 2021 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. |
spellingShingle | Article Lee, Kook Ra, Ho Lee, Jun Hyuk Baek, Jiwon Lee, Won Ki Classification of Pachychoroid on Optical Coherence Tomographic En Face Images Using Deep Convolutional Neural Networks |
title | Classification of Pachychoroid on Optical Coherence Tomographic En
Face Images Using Deep Convolutional Neural Networks |
title_full | Classification of Pachychoroid on Optical Coherence Tomographic En
Face Images Using Deep Convolutional Neural Networks |
title_fullStr | Classification of Pachychoroid on Optical Coherence Tomographic En
Face Images Using Deep Convolutional Neural Networks |
title_full_unstemmed | Classification of Pachychoroid on Optical Coherence Tomographic En
Face Images Using Deep Convolutional Neural Networks |
title_short | Classification of Pachychoroid on Optical Coherence Tomographic En
Face Images Using Deep Convolutional Neural Networks |
title_sort | classification of pachychoroid on optical coherence tomographic en
face images using deep convolutional neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8255502/ https://www.ncbi.nlm.nih.gov/pubmed/34185057 http://dx.doi.org/10.1167/tvst.10.7.28 |
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