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Home > Archive>Volume 33, Issue 3, 2024 >12-23. DOI:10.15888/j.cnki.csa.009421
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Research Progress in Segmentation of Retinal Blood Vessel Images Based on Deep Learning
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    Abstract:

    Retinal blood vessel image segmentation has a good auxiliary diagnostic effect on various eye diseases such as glaucoma and diabetic retinopathy. Currently, deep learning, with its powerful ability to discover abstract features, is expected to meet people’s needs for extracting feature information from retinal blood vessel images for automatic image segmentation. It has become a research hotspot in the field of retinal blood vessel image segmentation. To better grasp the research progress in this field, this study summarizes the relevant datasets and evaluation indicators and elaborates in detail on the application of deep learning in retinal blood vessel image segmentation. It focuses on the basic ideas, network structure, and improvements of various segmentation methods, analyzing the limitations and challenges faced by existing retinal blood vessel image segmentation methods and looking forward to the future research direction in this field.

    Reference
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贺鑫,王晓燕,周启香,张文凯.基于深度学习的眼底血管图像分割研究进展.计算机系统应用,2024,33(3):12-23

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  • Received:September 13,2023
  • Revised:October 08,2023
  • Online: December 25,2023
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