Bayesian Fully Convolutional Networks for Brain Image Registration Article Swipe
Kunpeng Cui
,
Panpan Fu
,
Yinghao Li
,
Yusong Lin
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1155/2021/5528160
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1155/2021/5528160
The purpose of medical image registration is to find geometric transformations that align two medical images so that the corresponding voxels on two images are spatially consistent. Nonrigid medical image registration is a key step in medical image processing, such as image comparison, data fusion, target recognition, and pathological change analysis. Existing registration methods only consider registration accuracy but largely neglect the uncertainty of registration results. In this work, a method based on the Bayesian fully convolutional neural network is proposed for nonrigid medical image registration. The proposed method can generate a geometric uncertainty map to calculate the uncertainty of registration results. This uncertainty can be interpreted as a confidence interval, which is essential for judging whether the source data are abnormal. Moreover, the proposed method introduces group normalization, which is conducive to the network convergence of the Bayesian neural network. Some representative learning-based image registration methods are compared with the proposed method on different image datasets. Experimental results show that the registration accuracy of the proposed method is better than that of the methods, and its antifolding performance is comparable to that of fast image registration and VoxelMorph. Furthermore, the proposed method can evaluate the uncertainty of registration results.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2021/5528160
- https://downloads.hindawi.com/journals/jhe/2021/5528160.pdf
- OA Status
- hybrid
- Cited By
- 9
- References
- 67
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3185462999
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3185462999Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2021/5528160Digital Object Identifier
- Title
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Bayesian Fully Convolutional Networks for Brain Image RegistrationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-07-26Full publication date if available
- Authors
-
Kunpeng Cui, Panpan Fu, Yinghao Li, Yusong LinList of authors in order
- Landing page
-
https://doi.org/10.1155/2021/5528160Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/jhe/2021/5528160.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://downloads.hindawi.com/journals/jhe/2021/5528160.pdfDirect OA link when available
- Concepts
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Image registration, Artificial intelligence, Computer science, Convolutional neural network, Computer vision, Voxel, Bayesian probability, Pattern recognition (psychology), Medical imaging, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
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9Total citation count in OpenAlex
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2025: 1, 2024: 2, 2023: 1, 2022: 3, 2021: 2Per-year citation counts (last 5 years)
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67Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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