Abstract
The segmentation results of retinal vessels can be an essential foundation for diagnosing and detecting numerous clinical medical symptoms. The work in this paper is to first train on the public dataset DRIVE to obtain the U-Net model. Secondly, the model is applied to private databases to segment fundus vascular images. Finally, deep learning metrics are evaluated on the U-Net model and test results. The experiment results demonstrate the effectiveness of U-Net on vessel segmentation tasks.
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Fang, Z., Jiang, H., Zhang, C. (2023). Investigation of Vessel Segmentation by U-Net Based on Numerous Datasets. In: Patnaik, S., Kountchev, R., Tai, Y., Kountcheva, R. (eds) 3D Imaging—Multidimensional Signal Processing and Deep Learning. Smart Innovation, Systems and Technologies, vol 348. Springer, Singapore. https://doi.org/10.1007/978-981-99-1145-5_13
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DOI: https://doi.org/10.1007/978-981-99-1145-5_13
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