Cell shape characterization and classification with discrete Fourier transforms and self-organizing maps.

Cells adapt their shapes in response to environmental changes, a process critical for function and survival. Traditional analysis focuses on movement patterns, using kinetic parameters to classify behavior. Expert knowledge supports these classifications, but automation is needed for large datasets. Cell shape itself contains valuable functional information, yet it is often overlooked. Combining shape and motion analysis could reveal new correlations. Current methods rely on 3D imaging and tracking, which are time-consuming and subjective. A more efficient and unbiased approach is required. This gap motivated the development of new shape-characterization tools. The need for scalable, automated methods is growing with increasing imaging data.

The study aims to develop a new method for classifying cell shapes using Discrete Fourier Transforms and Self-Organizing Maps. This approach seeks to reduce bias and increase efficiency in shape analysis. It addresses the limitations of traditional kinetic analysis alone. The method uses 3D-to-2D projections to simplify shape characterization. Fourier components are extracted to describe cell morphology. These components are then used to train a SOM for clustering. The goal is to provide an automated alternative to manual classification. This could improve the analysis of complex intravital imaging data.

The method begins with intravital multi-photon microscopy to capture cell shapes. Surface-rendered cells are projected into 2D for shape analysis. Discrete Fourier Transforms are applied to these projections. The DFT generates shape factors representing morphological features. These factors are used as input for a Self-Organizing Map. The SOM organizes the data into clusters based on shape similarity. This reduces the complexity of 3D shape analysis. The approach avoids reliance on kinetic parameters alone.

The DFT-based method successfully captured complex cell shapes in 2D projections. Fourier components provided detailed shape descriptors. The SOM effectively clustered cells according to shape characteristics. This clustering was unbiased and automated. The method outperformed traditional kinetic analysis in some cases. It identified shape-based groupings not evident from movement alone. The results suggest shape analysis can complement or replace kinetic methods. This approach is particularly useful when live imaging is impractical.

The study demonstrates that DFT and SOM can classify cell shapes efficiently. This method reduces bias and increases automation in shape analysis. It complements traditional kinetic approaches. Shape-based clustering reveals new patterns in cell behavior. The approach is suitable for large-scale intravital imaging datasets. It is especially valuable when live tracking is not feasible. The authors propose that shape analysis should be integrated with kinetic data. This could enhance the interpretation of complex cell behavior.