Multiparametric Magnetic Resonance Imaging Information Fusion Using Graph Convolutional Network for Glioma Grading Article Swipe
Peiying Guo
,
Longfei Li
,
Cheng Li
,
Weijian Huang
,
Guohua Zhao
,
Shanshan Wang
,
Meiyun Wang
,
Yusong Lin
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/7315665
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/7315665
Accurate preoperative glioma grading is essential for clinical decision-making and prognostic evaluation. Multiparametric magnetic resonance imaging (mpMRI) serves as an important diagnostic tool for glioma patients due to its superior performance in describing noninvasively the contextual information in tumor tissues. Previous studies achieved promising glioma grading results with mpMRI data utilizing a convolutional neural network (CNN)-based method. However, these studies have not fully exploited and effectively fused the rich tumor contextual information provided in the magnetic resonance (MR) images acquired with different imaging parameters. In this paper, a novel graph convolutional network (GCN)-based mpMRI information fusion module (named MMIF-GCN) is proposed to comprehensively fuse the tumor grading relevant information in mpMRI. Specifically, a graph is constructed according to the characteristics of mpMRI data. The vertices are defined as the glioma grading features of different slices extracted by the CNN, and the edges reflect the distances between the slices in a 3D volume. The proposed method updates the information in each vertex considering the interaction between adjacent vertices. The final glioma grading is conducted by combining the fused information in all vertices. The proposed MMIF-GCN module can introduce an additional nonlinear representation learning step in the process of mpMRI information fusion while maintaining the positional relationship between adjacent slices. Experiments were conducted on two datasets, that is, a public dataset (named BraTS2020) and a private one (named GliomaHPPH2018). The results indicate that the proposed method can effectively fuse the grading information provided in mpMRI data for better glioma grading performance.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2022/7315665
- https://downloads.hindawi.com/journals/jhe/2022/7315665.pdf
- OA Status
- hybrid
- Cited By
- 7
- References
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- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4280527660
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4280527660Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2022/7315665Digital Object Identifier
- Title
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Multiparametric Magnetic Resonance Imaging Information Fusion Using Graph Convolutional Network for Glioma GradingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-05-10Full publication date if available
- Authors
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Peiying Guo, Longfei Li, Cheng Li, Weijian Huang, Guohua Zhao, Shanshan Wang, Meiyun Wang, Yusong LinList of authors in order
- Landing page
-
https://doi.org/10.1155/2022/7315665Publisher landing page
- PDF URL
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https://downloads.hindawi.com/journals/jhe/2022/7315665.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://downloads.hindawi.com/journals/jhe/2022/7315665.pdfDirect OA link when available
- Concepts
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Grading (engineering), Glioma, Computer science, Convolutional neural network, Artificial intelligence, Magnetic resonance imaging, Graph, Information fusion, Pattern recognition (psychology), Radiology, Medicine, Theoretical computer science, Civil engineering, Cancer research, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
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2025: 3, 2024: 3, 2023: 1Per-year citation counts (last 5 years)
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43Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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