Skeletonization and fractal analysis of microglial cells in neonatal brain

Abstract

Microglial cells can assume a range of morphologies that exist on a continuum, spanning from ramified, to bushy, to an amoeboid phenotype. Studies show that the morphology of microglial cells can be correlated with severity of injury and response to treatment in traumatic brain injury and neurodegenerative diseases such as Alzheimer’s disease, underpinning the importance of understanding microglial morphology. In recent years, fractal dimension has become a proven measure of microglial morphology. However, research that analyzes fractal dimension often requires multiple software platforms to perform the analysis, making the analysis inefficient and computationally expensive. In this work, we introduce a compiled, automated Python package to analyze microglial morphology called Skeletonized Cell Analysis of Regional Features, or SCARF. By applying proper thresholding techniques, images are segmented, binarized, and skeletonized to simplify the complicated cell structure of microglia. Individual objects are then accessed via a number of morphological features and compared between different treatment groups or brain regions. From this Python package, we can also measure the fractal dimension of extracted skeletonized objects to quantify the morphological complexity of the microglial cells in response to injury and treatment. This study serves as an inspiration for future automated work in image analysis, as an easily obtainable package for relevant researchers, with gentle learning curve and can be easily customized to accommodate different field of study.