Unsupervised cell identification on multidimensional x-ray fluorescence datasets

Siwei Wang, Jesse Ward, Sven Leyffer, Stefan Wild, Chris Jacobsen, Stefan Vogt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

X-ray fluorescence microscopy is a powerful technique to map and quantify trace element distributions in biological specimens. It is perfectly placed to map nanoparticles and nanovectors within cells, at high spatial resolution. Advances in instrumentation, such as faster detectors, better optics, and improved data acquisition strategies are fundamentally changing the way experiments can be carried out, giving us the ability to more completely interrogate samples, at higher spatial resolution, higher throughput and better sensitivity. Yet one thing is still missing: The next generation of data analysis and visualization tools for multidimensional microscopy that can interpret data, identify and classify objects within datasets, visualize trends across datasets and instruments, and ultimately enable researchers to reason with abstraction of data instead of just with images.

Original languageEnglish (US)
Title of host publicationACM SIGGRAPH 2013 Posters, SIGGRAPH 2013
DOIs
StatePublished - Aug 21 2013
EventACM Special Interest Group on Computer Graphics and Interactive Techniques Conference, SIGGRAPH 2013 - Anaheim, CA, United States
Duration: Jul 21 2013Jul 25 2013

Other

OtherACM Special Interest Group on Computer Graphics and Interactive Techniques Conference, SIGGRAPH 2013
CountryUnited States
CityAnaheim, CA
Period7/21/137/25/13

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Software

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  • Cite this

    Wang, S., Ward, J., Leyffer, S., Wild, S., Jacobsen, C., & Vogt, S. (2013). Unsupervised cell identification on multidimensional x-ray fluorescence datasets. In ACM SIGGRAPH 2013 Posters, SIGGRAPH 2013 [88] https://doi.org/10.1145/2503385.2503481