Handle URI:
http://hdl.handle.net/10754/599607
Title:
Synthetic Brainbows
Authors:
Wan, Y.; Otsuna, H.; Hansen, C.
Abstract:
Brainbow is a genetic engineering technique that randomly colorizes cells. Biological samples processed with this technique and imaged with confocal microscopy have distinctive colors for individual cells. Complex cellular structures can then be easily visualized. However, the complexity of the Brainbow technique limits its applications. In practice, most confocal microscopy scans use different florescence staining with typically at most three distinct cellular structures. These structures are often packed and obscure each other in rendered images making analysis difficult. In this paper, we leverage a process known as GPU framebuffer feedback loops to synthesize Brainbow-like images. In addition, we incorporate ID shuffing and Monte-Carlo sampling into our technique, so that it can be applied to single-channel confocal microscopy data. The synthesized Brainbow images are presented to domain experts with positive feedback. A user survey demonstrates that our synthetic Brainbow technique improves visualizations of volume data with complex structures for biologists.
Citation:
Wan Y, Otsuna H, Hansen C (2013) Synthetic Brainbows. Computer Graphics Forum 32: 471–480. Available: http://dx.doi.org/10.1111/cgf.12134.
Publisher:
Wiley-Blackwell
Journal:
Computer Graphics Forum
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
Jun-2013
DOI:
10.1111/cgf.12134
PubMed ID:
25018576
PubMed Central ID:
PMC4091929
Type:
Article
ISSN:
0167-7055
Sponsors:
This research was sponsored by NIH-1R01GM098151-01, the DOE NNSA Award DE-NA0000740, KUS-C1-016-04 made by King Abdullah University of Science and Technology (KAUST), DOE SciDAC Institute of Scalable Data Management Analysis and Visualization DOE DE-SC0007446, NSF OCI-0906379, NSF IIS-1162013. We would like to thank A. Kelsey Lewis and her colleagues of the Department of Human Genetics at the University of Utah for a good explanation of the Brainbow technique. We also want to thank all the biologists participated in the survey. The reviewers' comments are very encouraging and helpful to us.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorWan, Y.en
dc.contributor.authorOtsuna, H.en
dc.contributor.authorHansen, C.en
dc.date.accessioned2016-02-28T06:30:45Zen
dc.date.available2016-02-28T06:30:45Zen
dc.date.issued2013-06en
dc.identifier.citationWan Y, Otsuna H, Hansen C (2013) Synthetic Brainbows. Computer Graphics Forum 32: 471–480. Available: http://dx.doi.org/10.1111/cgf.12134.en
dc.identifier.issn0167-7055en
dc.identifier.pmid25018576en
dc.identifier.doi10.1111/cgf.12134en
dc.identifier.urihttp://hdl.handle.net/10754/599607en
dc.description.abstractBrainbow is a genetic engineering technique that randomly colorizes cells. Biological samples processed with this technique and imaged with confocal microscopy have distinctive colors for individual cells. Complex cellular structures can then be easily visualized. However, the complexity of the Brainbow technique limits its applications. In practice, most confocal microscopy scans use different florescence staining with typically at most three distinct cellular structures. These structures are often packed and obscure each other in rendered images making analysis difficult. In this paper, we leverage a process known as GPU framebuffer feedback loops to synthesize Brainbow-like images. In addition, we incorporate ID shuffing and Monte-Carlo sampling into our technique, so that it can be applied to single-channel confocal microscopy data. The synthesized Brainbow images are presented to domain experts with positive feedback. A user survey demonstrates that our synthetic Brainbow technique improves visualizations of volume data with complex structures for biologists.en
dc.description.sponsorshipThis research was sponsored by NIH-1R01GM098151-01, the DOE NNSA Award DE-NA0000740, KUS-C1-016-04 made by King Abdullah University of Science and Technology (KAUST), DOE SciDAC Institute of Scalable Data Management Analysis and Visualization DOE DE-SC0007446, NSF OCI-0906379, NSF IIS-1162013. We would like to thank A. Kelsey Lewis and her colleagues of the Department of Human Genetics at the University of Utah for a good explanation of the Brainbow technique. We also want to thank all the biologists participated in the survey. The reviewers' comments are very encouraging and helpful to us.en
dc.publisherWiley-Blackwellen
dc.titleSynthetic Brainbowsen
dc.typeArticleen
dc.identifier.journalComputer Graphics Forumen
dc.identifier.pmcidPMC4091929en
dc.contributor.institutionScientific Computing and Imaging Institute, University of Utah, USA.en
dc.contributor.institutionDepartment of Neurobiology and Anatomy, University of Utah, USA.en
kaust.grant.numberKUS-C1-016-04en

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