Journal
JOURNAL OF SYNCHROTRON RADIATION
Volume 21, Issue -, Pages 568-579Publisher
INT UNION CRYSTALLOGRAPHY
DOI: 10.1107/S1600577514001416
Keywords
X-ray fluorescence microscopy (XFM); unsupervised object recognition; cell identification; trace element distributions; modeling overlapping cells
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Funding
- US Department of Energy, Office of Science, Advanced Scientific Computing Research, and Basic Energy Sciences program [DE-AC02-06CH11357]
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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.
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