902 research outputs found
Attention Mechanisms in Medical Image Segmentation: A Survey
Medical image segmentation plays an important role in computer-aided
diagnosis. Attention mechanisms that distinguish important parts from
irrelevant parts have been widely used in medical image segmentation tasks.
This paper systematically reviews the basic principles of attention mechanisms
and their applications in medical image segmentation. First, we review the
basic concepts of attention mechanism and formulation. Second, we surveyed over
300 articles related to medical image segmentation, and divided them into two
groups based on their attention mechanisms, non-Transformer attention and
Transformer attention. In each group, we deeply analyze the attention
mechanisms from three aspects based on the current literature work, i.e., the
principle of the mechanism (what to use), implementation methods (how to use),
and application tasks (where to use). We also thoroughly analyzed the
advantages and limitations of their applications to different tasks. Finally,
we summarize the current state of research and shortcomings in the field, and
discuss the potential challenges in the future, including task specificity,
robustness, standard evaluation, etc. We hope that this review can showcase the
overall research context of traditional and Transformer attention methods,
provide a clear reference for subsequent research, and inspire more advanced
attention research, not only in medical image segmentation, but also in other
image analysis scenarios.Comment: Submitted to Medical Image Analysis, survey paper, 34 pages, over 300
reference
A Trio-Method for Retinal Vessel Segmentation using Image Processing
Inner Retinal neurons are a most essential part of the retina and they are
supplied with blood via retinal vessels. This paper primarily focuses on the
segmentation of retinal vessels using a triple preprocessing approach. DRIVE
database was taken into consideration and preprocessed by Gabor Filtering,
Gaussian Blur, and Edge Detection by Sobel and Pruning. Segmentation was driven
out by 2 proposed U-Net architectures. Both the architectures were compared in
terms of all the standard performance metrics. Preprocessing generated varied
interesting results which impacted the results shown by the UNet architectures
for segmentation. This real-time deployment can help in the efficient
pre-processing of images with better segmentation and detection.Comment: Accepted at 26th UK Conference on Medical Image Understanding and
Analysis (MIUA-2022) (Abstract short paper
FAU-net: Fixup initialization channel attention neural network for complex blood vessel segmentation
© 2020 by the authors. Medical image segmentation based on deep learning is a central research issue in the field of computer vision. Many existing segmentation networks can achieve accurate segmentation using fewer data sets. However, they have disadvantages such as poor network flexibility and do not adequately consider the interdependence between feature channels. In response to these problems, this paper proposes a new de-normalized channel attention network, which uses an improved de-normalized residual block structure and a new channel attention module in the network for the segmentation of sophisticated vessels. The de-normalized network sends the extracted rough features to the channel attention network. The channel attention module can explicitly model the interdependence between channels and pay attention to the correlation with crucial information inmultiple feature channels. It can focus on the channels with the most association with vital information among multiple feature channels, and get more detailed feature results. Experimental results show that the network proposed in this paper is feasible, is robust, can accurately segment blood vessels, and is particularly suitable for complex blood vessel structures. Finally, we compared and verified the network proposed in this paper with the state-of-the-art network and obtained better experimental results
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