Enhancement of Perivascular Spaces Using Densely Connected Deep Convolutional Neural Network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jung, Euijin | - |
dc.contributor.author | Chikontwe, Philip | - |
dc.contributor.author | Zong, Xiaopeng | - |
dc.contributor.author | Lin, Weili | - |
dc.contributor.author | Shen, Dinggang | - |
dc.contributor.author | Park, Sang Hyun | - |
dc.date.accessioned | 2021-09-01T22:43:10Z | - |
dc.date.available | 2021-09-01T22:43:10Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2019 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/68907 | - |
dc.description.abstract | Perivascular spaces (PVS) in the human brain are related to various brain diseases. However, it is difficult to quantify them due to their thin and blurry appearance. In this paper, we introduce a deeplearning-based method, which can enhance a magnetic resonance (MR) image to better visualize the PVS. To accurately predict the enhanced image, we propose a very deep 3D convolutional neural network that contains densely connected networks with skip connections. The proposed networks can utilize rich contextual information derived from low-level to high-level features and effectively alleviate the gradient vanishing problem caused by the deep layers. The proposed method is evaluated on 17 7T MR images by a twofold cross-validation. The experiments show that our proposed network is much more effective to enhance the PVS than the previous PVS enhancement methods. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | VIRCHOW-ROBIN SPACES | - |
dc.subject | SEGMENTATION | - |
dc.subject | BRAIN | - |
dc.subject | MRI | - |
dc.subject | VISUALIZATION | - |
dc.subject | DISEASE | - |
dc.subject | MARKER | - |
dc.subject | MODEL | - |
dc.title | Enhancement of Perivascular Spaces Using Densely Connected Deep Convolutional Neural Network | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shen, Dinggang | - |
dc.identifier.doi | 10.1109/ACCESS.2019.2896911 | - |
dc.identifier.scopusid | 2-s2.0-85062226110 | - |
dc.identifier.wosid | 000459588300001 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.7, pp.18382 - 18391 | - |
dc.relation.isPartOf | IEEE ACCESS | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 7 | - |
dc.citation.startPage | 18382 | - |
dc.citation.endPage | 18391 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | VIRCHOW-ROBIN SPACES | - |
dc.subject.keywordPlus | SEGMENTATION | - |
dc.subject.keywordPlus | BRAIN | - |
dc.subject.keywordPlus | MRI | - |
dc.subject.keywordPlus | VISUALIZATION | - |
dc.subject.keywordPlus | DISEASE | - |
dc.subject.keywordPlus | MARKER | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordAuthor | Perivascular spaces | - |
dc.subject.keywordAuthor | MRI enhancement | - |
dc.subject.keywordAuthor | deep convolutional neural network | - |
dc.subject.keywordAuthor | densely connected network | - |
dc.subject.keywordAuthor | skip connections | - |
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