DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoon, Young-Gyu | ko |
dc.contributor.author | Dai, Peilun | ko |
dc.contributor.author | Wohlwend, Jeremy | ko |
dc.contributor.author | Chang, Jae-Byum | ko |
dc.contributor.author | Marblestone, Adam H. | ko |
dc.contributor.author | Boyden, Edward S. | ko |
dc.date.accessioned | 2018-09-18T06:24:47Z | - |
dc.date.available | 2018-09-18T06:24:47Z | - |
dc.date.created | 2018-09-04 | - |
dc.date.created | 2018-09-04 | - |
dc.date.created | 2018-09-04 | - |
dc.date.created | 2018-09-04 | - |
dc.date.created | 2018-09-04 | - |
dc.date.issued | 2017-10 | - |
dc.identifier.citation | FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, v.11 | - |
dc.identifier.issn | 1662-5188 | - |
dc.identifier.uri | http://hdl.handle.net/10203/245587 | - |
dc.description.abstract | We here introduce and study the properties, via computer simulation, of a candidate automated approach to algorithmic reconstruction of dense neural morphology, based on simulated data of the kind that would be obtained via two emerging molecular technologies-expansion microscopy (ExM) and in-situ molecular barcoding. We utilize a convolutional neural network to detect neuronal boundaries from protein-tagged plasma membrane images obtained via ExM, as well as a subsequent supervoxel-merging pipeline guided by optical readout of information-rich, cell-specific nucleic acid barcodes. We attempt to use conservative imaging and labeling parameters, with the goal of establishing a baseline case that points to the potential feasibility of optical circuit reconstruction, leaving open the possibility of higher-performance labeling technologies and algorithms. We find that, even with these conservative assumptions, an all-optical approach to dense neural morphology reconstruction may be possible via the proposed algorithmic framework. Future work should explore both the design-space of chemical labels and barcodes, aswell as algorithms, to ultimately enable routine, high-performance optical circuit reconstruction. | - |
dc.language | English | - |
dc.publisher | FRONTIERS MEDIA SA | - |
dc.title | Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration | - |
dc.type | Article | - |
dc.identifier.wosid | 000413539600001 | - |
dc.type.rims | ART | - |
dc.citation.volume | 11 | - |
dc.citation.publicationname | FRONTIERS IN COMPUTATIONAL NEUROSCIENCE | - |
dc.identifier.doi | 10.3389/fncom.2017.00097 | - |
dc.contributor.localauthor | Yoon, Young-Gyu | - |
dc.contributor.localauthor | Chang, Jae-Byum | - |
dc.contributor.nonIdAuthor | Dai, Peilun | - |
dc.contributor.nonIdAuthor | Wohlwend, Jeremy | - |
dc.contributor.nonIdAuthor | Marblestone, Adam H. | - |
dc.contributor.nonIdAuthor | Boyden, Edward S. | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | neural morphology | - |
dc.subject.keywordAuthor | 3-D reconstruction | - |
dc.subject.keywordAuthor | expansion microscopy | - |
dc.subject.keywordAuthor | RNA barcode | - |
dc.subject.keywordAuthor | convolutional neural network | - |
dc.subject.keywordAuthor | agglomeration | - |
dc.subject.keywordPlus | LIGHT-MICROSCOPY | - |
dc.subject.keywordPlus | HIGH-THROUGHPUT | - |
dc.subject.keywordPlus | FLUORESCENT PROTEINS | - |
dc.subject.keywordPlus | POLYACRYLAMIDE | - |
dc.subject.keywordPlus | RESOLUTION | - |
dc.subject.keywordPlus | CELLS | - |
dc.subject.keywordPlus | RNA | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.