VESDNet: Deep Vessel Segmentation (U) Network for the Early Diagnosis of Diabetic Retinopathy

Kumaran, Giridhar; Chelliah, Balika J.; Kumar, Shailesh

doi:10.1007/978-981-16-1244-2_32

Cited by 2 publications

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Deep learning technology in vascular image segmentation and disease diagnosis

Du,

Zhuang,

Huang

2024

Journal of Intelligent Medicine

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Blood vessel segmentation is a crucial aspect of medical image processing, aiding medical professionals in more accurate disease analysis and diagnosis. Manual blood vessel segmentation methods are time‐consuming and cumbersome, making the development of automatic segmentation methods essential. The rapid advancements in deep learning technology have introduced new tools and methods for vascular image segmentation. In this review, we provide a comprehensive overview of deep learning‐based blood vessel segmentation methods across various fields, including retinal vessel segmentation, cerebrovascular segmentation, and pulmonary vessel segmentation. Several prevalent diseases, such as retinal vascular diseases, cerebrovascular diseases, pulmonary vascular diseases, and tumors, have posed significant health challenges globally. This review also discusses the application of deep learning technology in disease diagnosis within these contexts. Finally, considering the current research landscape, we discuss existing challenges and potential future developments in blood vessel segmentation. We aim to assist researchers in gaining a comprehensive understanding and designing effective blood vessel segmentation models, ultimately offering opportunities for early disease diagnosis and treatment.

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