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Automated image analysis software for the study and quantification of retinal glial cells.
Miguel A Sánchez-Puebla1, Lidia Sánchez-Puebla2, Ana Granados1
1Carlos III University of Madrid, Computer Science Department, 28911, Leganés, Spain.
Computers in Biology and Medicine
|June 28, 2025
Summary
We developed new software for automated microglia analysis, significantly speeding up research and improving accuracy. This tool enhances the study of neurodegenerative diseases by providing detailed cell characterization.
Area of Science:
- Neuroscience
- Immunology
- Computational Biology
Background:
- Microglia are crucial for central nervous system health and disease.
- Manual microglia analysis is slow and subjective.
- Existing automated tools lack comprehensive characterization, especially for retinal microglia.
Purpose of the Study:
- To develop and validate an automated software for comprehensive microglia image analysis.
- To enable automated skeletonization and arborization measurements for the first time.
- To improve the efficiency and reproducibility of microglia research.
Main Methods:
- Developed novel automated image analysis software for microglia.
- Integrated soma detection, quantification, characterization, skeletonization, and arborization measurement.
- Validated the software on 1,702 murine retinal fluorescence microscopy images (24,559 cells).
Main Results:
- The software achieved high accuracy, processing images over 1,000 times faster than manual methods.
- It demonstrated robustness across high- and low-quality images, with performance improvement upon exclusion of low-quality data.
- Automated skeletonization and arborization measurements were successfully implemented.
Conclusions:
- This software establishes a new standard for retinal microglia analysis, offering unprecedented efficiency and detail.
- It enhances dataset usability, optimizes sample usage, and reduces animal sacrifice.
- The tool is valuable for large-scale studies and advancing neurodegenerative disease research.
