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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| Formato: | article |
| Lenguaje: | ZH |
| Publicado: |
Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2021
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| Acceso en línea: | https://doaj.org/article/eab98b5bdb55414bbdf25d9b03634cb1 |
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| Sumario: | 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. |
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