Research Progress of Deep Learning in Retinal Vessel Segmentation

The retinal features obtained by retinal blood vessel segmentation can be used to assist the diagnosis of diabetic retinopathy and other ocular diseases. In recent years, the automatic segmentation algorithm of blood vessels based on deep learning has attracted a lot of research. The reason is that...

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Autor principal:	LI Lanlan1, ZHANG Xiaohui1, NIU Decao3, HU Yihuang1, ZHAO Tiesong1, WANG Dabiao2+
Formato:	article
Lenguaje:	ZH
Publicado:	Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 2021
Materias:	deep learning retinal vessel segmentation neural networks Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/eab98b5bdb55414bbdf25d9b03634cb1
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id	oai:doaj.org-article:eab98b5bdb55414bbdf25d9b03634cb1
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spelling	oai:doaj.org-article:eab98b5bdb55414bbdf25d9b03634cb12021-11-10T08:02:42ZResearch Progress of Deep Learning in Retinal Vessel Segmentation10.3778/j.issn.1673-9418.21030991673-9418https://doaj.org/article/eab98b5bdb55414bbdf25d9b03634cb12021-11-01T00:00:00Zhttp://fcst.ceaj.org/CN/abstract/abstract2947.shtmlhttps://doaj.org/toc/1673-9418The retinal features obtained by retinal blood vessel segmentation can be used to assist the diagnosis of diabetic retinopathy and other ocular diseases. In recent years, the automatic segmentation algorithm of blood vessels based on deep learning has attracted a lot of research. The reason is that the method can automatically extract image features and has the advantages of high accuracy and fast speed. This paper reviews the research on retinal blood vessel segmentation based on deep learning in recent years. It first discusses the establishment of fundus image databases, commonly used data enhancement, image preprocessing, and image slicing operations. Then, recent deep learning algorithms are classified as cascaded neural network, multi-path neural network, multi-scale neural network in the perspective of network architecture, and these networks are carried out introduction, comparison, performance analysis, complexity analysis and disadvantage analysis. Besides, the introduction of the research on the actual deployment of neural networks is also given. The results show that the data amount in the existing fundus image database is still limited, and the most commonly used methods of data enhancement and image preprocessing are respectively horizontal and vertical flipping and image gray-scaling. Observed from the performance achieved by existing research, cascaded and multi-path neural networks are more suitable for retinal vessel segmentation. Observed from the existing complexity, the inference time of many models can reach the millisecond level, and the computational consumption can reach below million. Observed from the shortcomings of existing algorithms, an algorithm can only solve part of the existing challenges. In the case of mobile device hardware resource constraints, light-weight neural network is a direction worthy of exploration.LI Lanlan1, ZHANG Xiaohui1, NIU Decao3, HU Yihuang1, ZHAO Tiesong1, WANG Dabiao2+Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Pressarticledeep learningretinal vessel segmentationneural networksElectronic computers. Computer scienceQA75.5-76.95ZHJisuanji kexue yu tansuo, Vol 15, Iss 11, Pp 2063-2076 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	deep learning retinal vessel segmentation neural networks Electronic computers. Computer science QA75.5-76.95
spellingShingle	deep learning retinal vessel segmentation neural networks Electronic computers. Computer science QA75.5-76.95 LI Lanlan1, ZHANG Xiaohui1, NIU Decao3, HU Yihuang1, ZHAO Tiesong1, WANG Dabiao2+ Research Progress of Deep Learning in Retinal Vessel Segmentation
description	The retinal features obtained by retinal blood vessel segmentation can be used to assist the diagnosis of diabetic retinopathy and other ocular diseases. In recent years, the automatic segmentation algorithm of blood vessels based on deep learning has attracted a lot of research. The reason is that the method can automatically extract image features and has the advantages of high accuracy and fast speed. This paper reviews the research on retinal blood vessel segmentation based on deep learning in recent years. It first discusses the establishment of fundus image databases, commonly used data enhancement, image preprocessing, and image slicing operations. Then, recent deep learning algorithms are classified as cascaded neural network, multi-path neural network, multi-scale neural network in the perspective of network architecture, and these networks are carried out introduction, comparison, performance analysis, complexity analysis and disadvantage analysis. Besides, the introduction of the research on the actual deployment of neural networks is also given. The results show that the data amount in the existing fundus image database is still limited, and the most commonly used methods of data enhancement and image preprocessing are respectively horizontal and vertical flipping and image gray-scaling. Observed from the performance achieved by existing research, cascaded and multi-path neural networks are more suitable for retinal vessel segmentation. Observed from the existing complexity, the inference time of many models can reach the millisecond level, and the computational consumption can reach below million. Observed from the shortcomings of existing algorithms, an algorithm can only solve part of the existing challenges. In the case of mobile device hardware resource constraints, light-weight neural network is a direction worthy of exploration.
format	article
author	LI Lanlan1, ZHANG Xiaohui1, NIU Decao3, HU Yihuang1, ZHAO Tiesong1, WANG Dabiao2+
author_facet	LI Lanlan1, ZHANG Xiaohui1, NIU Decao3, HU Yihuang1, ZHAO Tiesong1, WANG Dabiao2+
author_sort	LI Lanlan1, ZHANG Xiaohui1, NIU Decao3, HU Yihuang1, ZHAO Tiesong1, WANG Dabiao2+
title	Research Progress of Deep Learning in Retinal Vessel Segmentation
title_short	Research Progress of Deep Learning in Retinal Vessel Segmentation
title_full	Research Progress of Deep Learning in Retinal Vessel Segmentation
title_fullStr	Research Progress of Deep Learning in Retinal Vessel Segmentation
title_full_unstemmed	Research Progress of Deep Learning in Retinal Vessel Segmentation
title_sort	research progress of deep learning in retinal vessel segmentation
publisher	Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
publishDate	2021
url	https://doaj.org/article/eab98b5bdb55414bbdf25d9b03634cb1
work_keys_str_mv	AT lilanlan1zhangxiaohui1niudecao3huyihuang1zhaotiesong1wangdabiao2 researchprogressofdeeplearninginretinalvesselsegmentation
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Research Progress of Deep Learning in Retinal Vessel Segmentation

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