Related Experiment Video
Updated: Feb 2, 2026
08:59
Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
Published on: October 28, 2018
7.6K
Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing
Fabian L Kriegel1, Ralf Köhler2, Jannike Bayat-Sarmadi2
1Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR); Deutsches Rheuma-Forschungszentrum (DRFZ) Berlin, a Leibniz Institute.
Journal of Visualized Experiments : Jove
|November 13, 2018
Summary
Researchers developed an AI toolkit to analyze 3D cell shapes from static images, enabling faster classification of healthy versus cancerous tissues based on microglial cell morphology. This simplifies complex 3D imaging challenges.
Area of Science:
- Cellular morphology and dynamics
- Computational biology and artificial intelligence
- Immunology and cancer research
Background:
- Immune cell behavior and appearance change in response to environmental stimuli, such as pathogen invasion and inflammation.
- Microglial cells in cancerous tissue exhibit altered morphokinetics, including less complex 3D shapes and increased motility compared to healthy tissue.
- Longitudinal 3D microscopy for morphokinetic analysis is challenging; static 3D shape analysis offers a simpler alternative.
Purpose of the Study:
- To develop analysis tools for fast and precise description of static 3D cell shapes.
- To enable diagnostic classification of healthy and pathogenic tissue samples based solely on static, shape-related information.
- To simplify the study of cell morphokinetics by analyzing static 3D shapes instead of complex longitudinal measurements.
Main Methods:
- A novel toolkit analyzes discrete Fourier components of 2D projections of 3D cell surfaces.
- Self-Organizing Maps (SOMs) are employed for shape analysis.
- Artificial intelligence methods are integrated to enable the framework to learn various cell shapes over time.
Main Results:
- The toolkit provides fast and precise descriptions of 3D cell shapes from static images.
- The AI-driven framework demonstrates the potential for classifying tissue samples based on static cell shape features.
- The workflow is designed to be simple, overcoming the challenges of complex longitudinal 3D microscopy.
Conclusions:
- Static 3D cell shape analysis using AI-powered Fourier component analysis is a viable method for tissue diagnostics.
- This approach simplifies the assessment of microglial cell morphokinetics in cancerous versus healthy tissues.
- The developed toolkit offers a promising solution for efficient and accurate classification of tissue samples based on cellular morphology.
Related Concept Videos
01:10Fast Fourier Transform
944
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
The computational efficiency of the FFT becomes...
944
01:21Properties of Fourier Transform I
660
The application of Fourier Transform properties in radio broadcasting is multifaceted, enabling significant advancements in the way signals are transmitted and received. Key areas where these properties are utilized include simultaneous multi-channel transmission, audio clip speed adjustments, live broadcast delays for different time zones, audio frequency adjustments, and signal demodulation.
In radio broadcasting, multiple audio signals often need to be transmitted simultaneously. The Fourier...
In radio broadcasting, multiple audio signals often need to be transmitted simultaneously. The Fourier...
660
01:24Properties of Fourier Transform II
774
The Fourier Transform (FT) is an essential mathematical tool in signal processing, transforming a time-domain signal into its frequency-domain representation. This transformation elucidates the relationship between time and frequency domains through several properties, each revealing unique aspects of signal behavior.
The Frequency Shifting property of Fourier Transforms highlights that a shift in the frequency domain corresponds to a phase shift in the time domain. Mathematically, if x(t) has...
The Frequency Shifting property of Fourier Transforms highlights that a shift in the frequency domain corresponds to a phase shift in the time domain. Mathematically, if x(t) has...
774
01:15Discrete Fourier Transform
901
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
901
01:07Basic signals of Fourier Transform
939
The Fourier Transform is a pivotal mathematical tool in signal processing, enabling the transformation of time-domain signals into their frequency-domain representations. Among the numerous elements within this domain, certain functions like the sinc function, delta function, and exponential signals hold significant importance due to their unique properties and implications.
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
939
01:11Continuous -time Fourier Transform
908
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
908