Paper
1 June 1992 Study of the cellular sociology through quantitative microscopy and topographical analysis
Christophe Dussert, Jacqueline Palmari, Monique Rasigni, Francis Kopp, Yolande Berthois, Xue-Fen Dong, Daniel Isnardon, Georges Rasigni, Pierre-Marie Martin
Author Affiliations +
Abstract
We have developed a methodology to quantitatively study tumor cell heterogeneity from a topographical point of view through the concept of a minimal spanning tree graph. This concept is applied to the quantitation of the degree of order that may exist in a cell population, and by combining biological and mathematical approaches, to the analysis of dynamic and metabolic interactions responsible for this topographical organization. The method is used to analyze the cell cycle phases in tumor cell lines: the cells are detected from an optical microscopy image of the preparation by using algorithms that preserve the cell topography. The cells appear to be differently, and non-randomly, spatially distributed depending on the cycle phase in which they fall. Those topographical behaviors allow us to deduce some unexpected proliferating characteristics of the cells and to compare them to a numerical model of the cell cycle in an interactive population, developed from the cellular automata theory. The method may as well be applied to the topographical analysis of the cells expressing hormone receptors (namely, oestrogenic ones). More generally it may be used to analyze and quantify the cellular sociology both in its normal (morphogenesis) and pathological (cancer, therapeutic responses, ...) aspects.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Christophe Dussert, Jacqueline Palmari, Monique Rasigni, Francis Kopp, Yolande Berthois, Xue-Fen Dong, Daniel Isnardon, Georges Rasigni, and Pierre-Marie Martin "Study of the cellular sociology through quantitative microscopy and topographical analysis", Proc. SPIE 1652, Medical Imaging VI: Image Processing, (1 June 1992); https://doi.org/10.1117/12.59467
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KEYWORDS
Image processing

Biological research

Medical imaging

Modulation

Microscopy

Monte Carlo methods

Tumors

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