A Novel Framework for Quantitative Analysis of Neuronal Primary Cilia in Brain Tissue.

Background: Accurate analysis of neuronal primary cilia is essential for understanding developmental processing of neurons. But existing image segmentation methods struggle with staining variability and background noise. To address this, we developed a more robust segmentation and statistical analysis pipeline using an animal model small sample size and with known neuronal microstructure alterations. Methods: Maternal obesity was induced in mice via a high-fat/high-sucrose diet. Hippocampal tissue from 6-month-old offspring of obese and control dams was analyzed. We developed a MATLAB-based pipeline to segment neuronal cilia from z-stack images, applying mathematical transformations and using the Weibull distribution and Bayesian Information Criterion (BIC) to assess group differences. Results: The technique segmented cilia despite artifacts, revealing group-specific patterns. Statistical analysis confirmed significant differences, highlighting the method's robustness over traditional tests, especially with small samples. Conclusion: Our method reliably segments neuronal primary cilia in immune-stained sections with thionin-counter staining and offers a sensitive, assumption-free alternative to traditional statistical tests, ideal for small-sample neurobiological studies.