Unsupervised cell identification on multidimensional X-ray fluorescence datasets.
J Synchrotron Radiat
; 21(Pt 3): 568-79, 2014 May.
Article
em En
| MEDLINE
| ID: mdl-24763647
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.
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Base de dados:
MEDLINE
Tipo de estudo:
Diagnostic_studies
Idioma:
En
Ano de publicação:
2014
Tipo de documento:
Article