Topological correction of infant white matter surfaces using anatomically constrained convolutional neural network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sun, Liang | - |
dc.contributor.author | Zhang, Daoqiang | - |
dc.contributor.author | Lian, Chunfeng | - |
dc.contributor.author | Wang, Li | - |
dc.contributor.author | Wu, Zhengwang | - |
dc.contributor.author | Shao, Wei | - |
dc.contributor.author | Lin, Weili | - |
dc.contributor.author | Shen, Dinggang | - |
dc.contributor.author | Li, Gang | - |
dc.date.accessioned | 2021-09-01T07:21:30Z | - |
dc.date.available | 2021-09-01T07:21:30Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2019-09 | - |
dc.identifier.issn | 1053-8119 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/62994 | - |
dc.description.abstract | Reconstruction of accurate cortical surfaces without topological errors (i.e., handles and holes) from infant brain MR images is very important in early brain development studies. However, infant brain MR images typically suffer extremely low tissue contrast and dynamic imaging appearance patterns. Thus, it is inevitable to have large amounts of topological errors in the segmented infant brain tissue images, which lead to inaccurately reconstructed cortical surfaces with topological errors. To address this issue, inspired by recent advances in deep learning, we propose an anatomically constrained network for topological correction on infant cortical surfaces. Specifically, in our method, we first locate regions of potential topological defects by leveraging a topology-preserving level set method. Then, we propose an anatomically constrained network to correct those candidate voxels in the located regions. Since infant cortical surfaces often contain large and complex handles or holes, it is difficult to completely correct all errors using one-shot correction. Therefore, we further enroll these two steps into an iterative framework to gradually correct large topological errors. To the best of our knowledge, this is the first work to introduce deep learning approach for topological correction of infant cortical surfaces. We compare our method with the state-of-the-art methods on both simulated topological errors and real topological errors in human infant brain MR images. Moreover, we also validate our method on the infant brain MR images of macaques. All experimental results show the superior performance of the proposed method. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ACADEMIC PRESS INC ELSEVIER SCIENCE | - |
dc.subject | AUTOMATED 3-D EXTRACTION | - |
dc.subject | GEOMETRICALLY ACCURATE | - |
dc.subject | SIMPLE POINTS | - |
dc.subject | BRAIN | - |
dc.subject | SEGMENTATION | - |
dc.subject | RECONSTRUCTION | - |
dc.subject | INTEGRATION | - |
dc.subject | MULTISCALE | - |
dc.subject | CORTEX | - |
dc.subject | INNER | - |
dc.title | Topological correction of infant white matter surfaces using anatomically constrained convolutional neural network | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shen, Dinggang | - |
dc.identifier.doi | 10.1016/j.neuroimage.2019.05.037 | - |
dc.identifier.scopusid | 2-s2.0-85065924616 | - |
dc.identifier.wosid | 000472495100011 | - |
dc.identifier.bibliographicCitation | NEUROIMAGE, v.198, pp.114 - 124 | - |
dc.relation.isPartOf | NEUROIMAGE | - |
dc.citation.title | NEUROIMAGE | - |
dc.citation.volume | 198 | - |
dc.citation.startPage | 114 | - |
dc.citation.endPage | 124 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Neurosciences & Neurology | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Neurosciences | - |
dc.relation.journalWebOfScienceCategory | Neuroimaging | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.subject.keywordPlus | AUTOMATED 3-D EXTRACTION | - |
dc.subject.keywordPlus | GEOMETRICALLY ACCURATE | - |
dc.subject.keywordPlus | SIMPLE POINTS | - |
dc.subject.keywordPlus | BRAIN | - |
dc.subject.keywordPlus | SEGMENTATION | - |
dc.subject.keywordPlus | RECONSTRUCTION | - |
dc.subject.keywordPlus | INTEGRATION | - |
dc.subject.keywordPlus | MULTISCALE | - |
dc.subject.keywordPlus | CORTEX | - |
dc.subject.keywordPlus | INNER | - |
dc.subject.keywordAuthor | Topological correction | - |
dc.subject.keywordAuthor | Anatomically constrained network | - |
dc.subject.keywordAuthor | Infant cortical surfaces | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.