Unsupervised cell identification on multidimensional X-ray fluorescence datasets

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

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


A novel approach to locate, identify and refine positions and whole areas of cell structures based on elemental contents measured by X-ray fluorescence microscopy is introduced. It is shown that, by initializing with only a handful of prototypical cell regions, this approach can obtain consistent identification of whole cells, even when cells are overlapping, without training by explicit annotation. It is robust both to different measurements on the same sample and to different initializations. This effort provides a versatile framework to identify targeted cellular structures from datasets too complex for manual analysis, like most X-ray fluorescence microscopy data. Possible future extensions are also discussed.

Original languageEnglish (US)
Pages (from-to)568-579
Number of pages12
JournalJournal of Synchrotron Radiation
Issue number3
StatePublished - Jan 1 2014


  • X-ray fluorescence microscopy (XFM)
  • cell identification
  • modeling overlapping cells
  • trace element distributions
  • unsupervised object recognition

ASJC Scopus subject areas

  • Radiation
  • Nuclear and High Energy Physics
  • Instrumentation


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