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Cell shape characterization and classification with discrete Fourier transforms and self‐organizing maps
Author(s) -
Kriegel Fabian L.,
Köhler Ralf,
BayatSarmadi Jannike,
Bayerl Simon,
Hauser Anja E.,
Niesner Raluca,
Luch Andreas,
Cseresnyes Zoltan
Publication year - 2018
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.23279
Subject(s) - biological system , computer science , characterization (materials science) , cluster analysis , fourier transform , artificial intelligence , self organizing map , a priori and a posteriori , discrete fourier transform (general) , fourier analysis , pattern recognition (psychology) , data mining , algorithm , materials science , mathematics , nanotechnology , biology , mathematical analysis , philosophy , epistemology , fractional fourier transform
Cells in their natural environment often exhibit complex kinetic behavior and radical adjustments of their shapes. This enables them to accommodate to short‐ and long‐term changes in their surroundings under physiological and pathological conditions. Intravital multi‐photon microscopy is a powerful tool to record this complex behavior. Traditionally, cell behavior is characterized by tracking the cells' movements, which yields numerous parameters describing the spatiotemporal characteristics of cells. Cells can be classified according to their tracking behavior using all or a subset of these kinetic parameters. This categorization can be supported by the a priori knowledge of experts. While such an approach provides an excellent starting point for analyzing complex intravital imaging data, faster methods are required for automated and unbiased characterization. In addition to their kinetic behavior, the 3D shape of these cells also provide essential clues about the cells' status and functionality. New approaches that include the study of cell shapes as well may also allow the discovery of correlations amongst the track‐ and shape‐describing parameters. In the current study, we examine the applicability of a set of Fourier components produced by Discrete Fourier Transform (DFT) as a tool for more efficient and less biased classification of complex cell shapes. By carrying out a number of 3D‐to‐2D projections of surface‐rendered cells, the applied method reduces the more complex 3D shape characterization to a series of 2D DFTs. The resulting shape factors are used to train a Self‐Organizing Map (SOM), which provides an unbiased estimate for the best clustering of the data, thereby characterizing groups of cells according to their shape. We propose and demonstrate that such shape characterization is a powerful addition to, or a replacement for kinetic analysis. This would make it especially useful in situations where live kinetic imaging is less practical or not possible at all. © 2017 International Society for Advancement of Cytometry