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Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning

Vision loss caused by diabetic macular edema (DME) can be prevented by early detection and laser photocoagulation. As there is no comprehensive detection technique to recognize NPA, we proposed an automatic detection method of NPA on fundus fluorescein angiography (FFA) in DME. The study included 3,...

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Autores principales: Jin, Kai, Pan, Xiangji, You, Kun, Wu, Jian, Liu, Zhifang, Cao, Jing, Lou, Lixia, Xu, Yufeng, Su, Zhaoan, Yao, Ke, Ye, Juan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7492239/
https://www.ncbi.nlm.nih.gov/pubmed/32934283
http://dx.doi.org/10.1038/s41598-020-71622-6
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author Jin, Kai
Pan, Xiangji
You, Kun
Wu, Jian
Liu, Zhifang
Cao, Jing
Lou, Lixia
Xu, Yufeng
Su, Zhaoan
Yao, Ke
Ye, Juan
author_facet Jin, Kai
Pan, Xiangji
You, Kun
Wu, Jian
Liu, Zhifang
Cao, Jing
Lou, Lixia
Xu, Yufeng
Su, Zhaoan
Yao, Ke
Ye, Juan
author_sort Jin, Kai
collection PubMed
description Vision loss caused by diabetic macular edema (DME) can be prevented by early detection and laser photocoagulation. As there is no comprehensive detection technique to recognize NPA, we proposed an automatic detection method of NPA on fundus fluorescein angiography (FFA) in DME. The study included 3,014 FFA images of 221 patients with DME. We use 3 convolutional neural networks (CNNs), including DenseNet, ResNet50, and VGG16, to identify non-perfusion regions (NP), microaneurysms, and leakages in FFA images. The NPA was segmented using attention U-net. To validate its performance, we applied our detection algorithm on 249 FFA images in which the NPA areas were manually delineated by 3 ophthalmologists. For DR lesion classification, area under the curve is 0.8855 for NP regions, 0.9782 for microaneurysms, and 0.9765 for leakage classifier. The average precision of NP region overlap ratio is 0.643. NP regions of DME in FFA images are identified based a new automated deep learning algorithm. This study is an in-depth study from computer-aided diagnosis to treatment, and will be the theoretical basis for the application of intelligent guided laser.
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spelling pubmed-74922392020-09-16 Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning Jin, Kai Pan, Xiangji You, Kun Wu, Jian Liu, Zhifang Cao, Jing Lou, Lixia Xu, Yufeng Su, Zhaoan Yao, Ke Ye, Juan Sci Rep Article Vision loss caused by diabetic macular edema (DME) can be prevented by early detection and laser photocoagulation. As there is no comprehensive detection technique to recognize NPA, we proposed an automatic detection method of NPA on fundus fluorescein angiography (FFA) in DME. The study included 3,014 FFA images of 221 patients with DME. We use 3 convolutional neural networks (CNNs), including DenseNet, ResNet50, and VGG16, to identify non-perfusion regions (NP), microaneurysms, and leakages in FFA images. The NPA was segmented using attention U-net. To validate its performance, we applied our detection algorithm on 249 FFA images in which the NPA areas were manually delineated by 3 ophthalmologists. For DR lesion classification, area under the curve is 0.8855 for NP regions, 0.9782 for microaneurysms, and 0.9765 for leakage classifier. The average precision of NP region overlap ratio is 0.643. NP regions of DME in FFA images are identified based a new automated deep learning algorithm. This study is an in-depth study from computer-aided diagnosis to treatment, and will be the theoretical basis for the application of intelligent guided laser. Nature Publishing Group UK 2020-09-15 /pmc/articles/PMC7492239/ /pubmed/32934283 http://dx.doi.org/10.1038/s41598-020-71622-6 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Jin, Kai
Pan, Xiangji
You, Kun
Wu, Jian
Liu, Zhifang
Cao, Jing
Lou, Lixia
Xu, Yufeng
Su, Zhaoan
Yao, Ke
Ye, Juan
Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning
title Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning
title_full Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning
title_fullStr Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning
title_full_unstemmed Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning
title_short Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning
title_sort automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7492239/
https://www.ncbi.nlm.nih.gov/pubmed/32934283
http://dx.doi.org/10.1038/s41598-020-71622-6
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