Argonne National Laboratory

Unsupervised Cell Identification on Multidimensional X-ray Fluorescence Datasets

TitleUnsupervised Cell Identification on Multidimensional X-ray Fluorescence Datasets
Publication TypeJournal Article
Year of Publication2013
AuthorsWang, S, Ward, J, Leyffer, S, Wild, SM, Jacobsen, C, Vogt, S
JournalJournal of Synchrotron Radiation
Date Published05/2014
Other NumbersANL/MCS-P4065-0413
AbstractWe introduce 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. We show that by initializing with only a handful of prototypical cell regions, this approach can obtain consistent cell populations, even when cells are partially 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. We also discuss possible future extensions.