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Automatic Method for Optic Disc Segmentation Using Deep Learning on Retinal Fundus Images

OBJECTIVES: The optic disc is part of the retinal fundus image structure, which influences the extraction of glaucoma features. This study proposes a method that automatically segments the optic disc area in retinal fundus images using deep learning based on a convolutional neural network (CNN). MET...

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Autores principales: Septiarini, Anindita, Hamdani, Hamdani, Setyaningsih, Emy, Junirianto, Eko, Utaminingrum, Fitri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Society of Medical Informatics 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10209731/
https://www.ncbi.nlm.nih.gov/pubmed/37190738
http://dx.doi.org/10.4258/hir.2023.29.2.145
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author Septiarini, Anindita
Hamdani, Hamdani
Setyaningsih, Emy
Junirianto, Eko
Utaminingrum, Fitri
author_facet Septiarini, Anindita
Hamdani, Hamdani
Setyaningsih, Emy
Junirianto, Eko
Utaminingrum, Fitri
author_sort Septiarini, Anindita
collection PubMed
description OBJECTIVES: The optic disc is part of the retinal fundus image structure, which influences the extraction of glaucoma features. This study proposes a method that automatically segments the optic disc area in retinal fundus images using deep learning based on a convolutional neural network (CNN). METHODS: This study used private and public datasets containing retinal fundus images. The private dataset consisted of 350 images, while the public dataset was the Retinal Fundus Glaucoma Challenge (REFUGE). The proposed method was based on a CNN with a single-shot multibox detector (MobileNetV2) to form images of the region-of-interest (ROI) using the original image resized into 640 × 640 input data. A pre-processing sequence was then implemented, including augmentation, resizing, and normalization. Furthermore, a U-Net model was applied for optic disc segmentation with 128 × 128 input data. RESULTS: The proposed method was appropriately applied to the datasets used, as shown by the values of the F1-score, dice score, and intersection over union of 0.9880, 0.9852, and 0.9763 for the private dataset, respectively, and 0.9854, 0.9838 and 0.9712 for the REFUGE dataset. CONCLUSIONS: The optic disc area produced by the proposed method was similar to that identified by an ophthalmologist. Therefore, this method can be considered for implementing automatic segmentation of the optic disc area.
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spelling pubmed-102097312023-05-26 Automatic Method for Optic Disc Segmentation Using Deep Learning on Retinal Fundus Images Septiarini, Anindita Hamdani, Hamdani Setyaningsih, Emy Junirianto, Eko Utaminingrum, Fitri Healthc Inform Res Original Article OBJECTIVES: The optic disc is part of the retinal fundus image structure, which influences the extraction of glaucoma features. This study proposes a method that automatically segments the optic disc area in retinal fundus images using deep learning based on a convolutional neural network (CNN). METHODS: This study used private and public datasets containing retinal fundus images. The private dataset consisted of 350 images, while the public dataset was the Retinal Fundus Glaucoma Challenge (REFUGE). The proposed method was based on a CNN with a single-shot multibox detector (MobileNetV2) to form images of the region-of-interest (ROI) using the original image resized into 640 × 640 input data. A pre-processing sequence was then implemented, including augmentation, resizing, and normalization. Furthermore, a U-Net model was applied for optic disc segmentation with 128 × 128 input data. RESULTS: The proposed method was appropriately applied to the datasets used, as shown by the values of the F1-score, dice score, and intersection over union of 0.9880, 0.9852, and 0.9763 for the private dataset, respectively, and 0.9854, 0.9838 and 0.9712 for the REFUGE dataset. CONCLUSIONS: The optic disc area produced by the proposed method was similar to that identified by an ophthalmologist. Therefore, this method can be considered for implementing automatic segmentation of the optic disc area. Korean Society of Medical Informatics 2023-04 2023-04-30 /pmc/articles/PMC10209731/ /pubmed/37190738 http://dx.doi.org/10.4258/hir.2023.29.2.145 Text en © 2023 The Korean Society of Medical Informatics https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Septiarini, Anindita
Hamdani, Hamdani
Setyaningsih, Emy
Junirianto, Eko
Utaminingrum, Fitri
Automatic Method for Optic Disc Segmentation Using Deep Learning on Retinal Fundus Images
title Automatic Method for Optic Disc Segmentation Using Deep Learning on Retinal Fundus Images
title_full Automatic Method for Optic Disc Segmentation Using Deep Learning on Retinal Fundus Images
title_fullStr Automatic Method for Optic Disc Segmentation Using Deep Learning on Retinal Fundus Images
title_full_unstemmed Automatic Method for Optic Disc Segmentation Using Deep Learning on Retinal Fundus Images
title_short Automatic Method for Optic Disc Segmentation Using Deep Learning on Retinal Fundus Images
title_sort automatic method for optic disc segmentation using deep learning on retinal fundus images
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10209731/
https://www.ncbi.nlm.nih.gov/pubmed/37190738
http://dx.doi.org/10.4258/hir.2023.29.2.145
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