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Updated: Jun 27, 2025

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

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Published on: October 28, 2018

Learning Morphological, Spatial, and Dynamic Models of Cellular Components.

Huangqingbo Sun¹, Robert F Murphy²

¹Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.

Methods in Molecular Biology (Clifton, N.J.)

|May 6, 2024

Summary

This summary is machine-generated.

CellOrganizer software on Jupyter Notebook analyzes cell and organelle structure. It builds statistical models from microscope images for cell organization and spatial arrangement analysis.

Keywords:

Cell organization Image analysis Image processing Machine learning Organelle modeling Organelle shape

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Area of Science:

Cell Biology
Computational Biology
Bioinformatics

Background:

Microscopy provides detailed cellular images.
Analyzing cell and organelle spatial organization is complex.
Statistical modeling can capture cellular structural variations.

Purpose of the Study:

To describe protocols for CellOrganizer software.
To analyze and model cell and organelle shape and spatial arrangement.
To utilize Jupyter Notebook for these analyses.

Main Methods:

Using CellOrganizer software within the Jupyter Notebook platform.
Learning statistical models from microscope images.
Modeling distributions of cell component number, shape, and spatial arrangement.

Main Results:

Developed protocols for CellOrganizer on Jupyter Notebook.
Created statistical models capturing cell and organelle organization.
Enabled comparison of spatial organization across cell types or conditions.

Conclusions:

CellOrganizer provides a framework for analyzing cellular spatial organization.
The generated models are generative and useful for simulations.
This approach facilitates understanding of cellular structure and variation.