BIRNet: Brain image registration using dual-supervised fully convolutional networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Fan, Jingfan | - |
dc.contributor.author | Cao, Xiaohuan | - |
dc.contributor.author | Yap, Pew-Thian | - |
dc.contributor.author | Shen, Dinggang | - |
dc.date.accessioned | 2021-09-01T15:38:28Z | - |
dc.date.available | 2021-09-01T15:38:28Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2019-05 | - |
dc.identifier.issn | 1361-8415 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/65811 | - |
dc.description.abstract | In this paper, we propose a deep learning approach for image registration by predicting deformation from image appearance. Since obtaining ground-truth deformation fields for training can be challenging, we design a fully convolutional network that is subject to dual-guidance: (1) Ground-truth guidance using deformation fields obtained by an existing registration method; and (2) Image dissimilarity guidance using the difference between the images after registration. The latter guidance helps avoid overly relying on the supervision from the training deformation fields, which could be inaccurate. For effective training, we further improve the deep convolutional network with gap filling, hierarchical loss, and multi-source strategies. Experiments on a variety of datasets show promising registration accuracy and efficiency compared with state-of-the-art methods. (C) 2019 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.subject | SYMMETRIC DIFFEOMORPHIC REGISTRATION | - |
dc.subject | CONSTRUCTION | - |
dc.subject | ROBUST | - |
dc.subject | ATLAS | - |
dc.subject | APPEARANCE | - |
dc.subject | HAMMER | - |
dc.title | BIRNet: Brain image registration using dual-supervised fully convolutional networks | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shen, Dinggang | - |
dc.identifier.doi | 10.1016/j.media.2019.03.006 | - |
dc.identifier.scopusid | 2-s2.0-85063570767 | - |
dc.identifier.wosid | 000467891200016 | - |
dc.identifier.bibliographicCitation | MEDICAL IMAGE ANALYSIS, v.54, pp.193 - 206 | - |
dc.relation.isPartOf | MEDICAL IMAGE ANALYSIS | - |
dc.citation.title | MEDICAL IMAGE ANALYSIS | - |
dc.citation.volume | 54 | - |
dc.citation.startPage | 193 | - |
dc.citation.endPage | 206 | - |
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 | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Biomedical | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.subject.keywordPlus | SYMMETRIC DIFFEOMORPHIC REGISTRATION | - |
dc.subject.keywordPlus | CONSTRUCTION | - |
dc.subject.keywordPlus | ROBUST | - |
dc.subject.keywordPlus | ATLAS | - |
dc.subject.keywordPlus | APPEARANCE | - |
dc.subject.keywordPlus | HAMMER | - |
dc.subject.keywordAuthor | Image registration | - |
dc.subject.keywordAuthor | Convolutional neural networks | - |
dc.subject.keywordAuthor | Brain MR image | - |
dc.subject.keywordAuthor | Hierarchical registration | - |
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