Fluorescence microscopy tensor imaging representations for large-scale dataset analysis
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Vinegoni, Claudio | - |
dc.contributor.author | Feruglio, Paolo Fumene | - |
dc.contributor.author | Courties, Gabriel | - |
dc.contributor.author | Schmidt, Stephen | - |
dc.contributor.author | Hulsmans, Maarten | - |
dc.contributor.author | Lee, Sungon | - |
dc.contributor.author | Wang, Rui | - |
dc.contributor.author | Sosnovik, David | - |
dc.contributor.author | Nahrendorf, Matthias | - |
dc.contributor.author | Weissleder, Ralph | - |
dc.date.accessioned | 2021-06-22T09:06:42Z | - |
dc.date.available | 2021-06-22T09:06:42Z | - |
dc.date.issued | 2020-03 | - |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/1210 | - |
dc.description.abstract | Understanding complex biological systems requires the system-wide characterization of cellular and molecular features. Recent advances in optical imaging technologies and chemical tissue clearing have facilitated the acquisition of whole-organ imaging datasets, but automated tools for their quantitative analysis and visualization are still lacking. We have here developed a visualization technique capable of providing whole-organ tensor imaging representations of local regional descriptors based on fluorescence data acquisition. This method enables rapid, multiscale, analysis and virtualization of large-volume, high-resolution complex biological data while generating 3D tractographic representations. Using the murine heart as a model, our method allowed us to analyze and interrogate the cardiac microvasculature and the tissue resident macrophage distribution and better infer and delineate the underlying structural network in unprecedented detail. | - |
dc.format.extent | 15 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | NATURE PUBLISHING GROUP | - |
dc.title | Fluorescence microscopy tensor imaging representations for large-scale dataset analysis | - |
dc.type | Article | - |
dc.publisher.location | 영국 | - |
dc.identifier.doi | 10.1038/s41598-020-62233-2 | - |
dc.identifier.scopusid | 2-s2.0-85082543790 | - |
dc.identifier.wosid | 000560408800011 | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, v.10, no.1, pp 1 - 15 | - |
dc.citation.title | SCIENTIFIC REPORTS | - |
dc.citation.volume | 10 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 15 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.subject.keywordPlus | SINGLE-CELL RESOLUTION | - |
dc.subject.keywordPlus | WHOLE-BRAIN | - |
dc.subject.keywordPlus | DIFFUSION | - |
dc.subject.keywordPlus | MRI | - |
dc.subject.keywordPlus | ORIENTATION | - |
dc.subject.keywordPlus | MICROSTRUCTURE | - |
dc.subject.keywordPlus | VISUALIZATION | - |
dc.subject.keywordPlus | TRACTOGRAPHY | - |
dc.subject.keywordPlus | VALIDATION | - |
dc.subject.keywordPlus | PRINCIPLES | - |
dc.identifier.url | https://www.nature.com/articles/s41598-020-62233-2 | - |
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