A retinal vessel segmentation method based improved U-Net model

Sun, Kening; Chen, Yang; Chen, Yi; Geng, Jiameng; Chen, Yinsheng

doi:10.1016/j.bspc.2023.104574

Cited by 10 publications

(1 citation statement)

References 32 publications

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“…Therefore, the training objective of the network model is to make the residual approach 0, while the accuracy does not decrease as the number of network layers increases. Utilize residual networks every few layers [21]. y=F(x,W i )+x (1) In the formula, x and y respectively represent the input and output vectors of the network layer, and the function F(x,Wi) is the residual mapping that the model needs to learn.…”

Section: A Residual Network With Gradient Jumpingmentioning

confidence: 99%

An Attention-Based Improved U-Net Neural Network Model for Semantic Segmentation of Moving Objects

Cui

2024

IEEE Access

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The precise semantic segmentation for moving targets has always been a challenging task in computer vision. The existing methods basically had some limitations in semantic segmentation, such as inability to handle deformation of moving targets, blurred boundaries, and other issues. To address the issue, this paper develops an improved U-Net model based on attention mechanism for this purpose. Firstly, we introduce an attention mechanism to enhance the perceptual ability of the U-Net model. By adding attention modules at different levels between the encoder and decoder, the network can pay more attention to the key features of moving targets at different levels. Then, we add a residual module to improve robustness and complete the capsule network for semantic segmentation of moving targets. By learning the deformation information of moving targets, the network can better adapt to moving targets with different shapes. We have conducted experimental verification on multiple public datasets. The experimental results show that the proposed method has superior performance in semantic segmentation tasks of moving targets. Compared with traditional U-Net-based models, the proposal shows significant improvements in accuracy and robustness.

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Section: A Residual Network With Gradient Jumpingmentioning

confidence: 99%

An Attention-Based Improved U-Net Neural Network Model for Semantic Segmentation of Moving Objects

Cui

2024

IEEE Access

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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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