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Unsupervised cell identification on multidimensional X‐ray fluorescence datasets
Authors:Siwei Wang  Jesse Ward  Sven Leyffer  Stefan M. Wild  Chris Jacobsen  Stefan Vogt
Abstract: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.
Keywords:X‐ray fluorescence microscopy (XFM)  unsupervised object recognition  cell   identification  trace element distributions  modeling overlapping cells
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