Quantitative Evaluation of Neurite Morphology Using Graph Structure

Abstract

Recently, the analysis of cellular images, particularly the assessment of neurite activity, has gained increasing significance in the study of neurodegenerative diseases, including Alzheimer’s disease. This study introduces an automated analysis approach that focuses on neurite activity through the application of cellular segmentation techniques to bright-field images of neurons. This study proposes a method for treating individual cell instances as graphs consisting of nodes and edges. Furthermore, this study suggests a quantitative assessment for precisely identified neurites through the definition of several evaluation metrics. This approach enables the fast and objective automated analysis of bright-field images focused on neurons. In a variety of experiments, the precision of our proposed method was verified through a comparative analysis by comparing the results to manual analysis data using ImageJ for measuring the neurite length of rat adrenal pheochromocytoma PC12 cells. The findings revealed that the average discrepancy in the length of neurites is only 4.387 μm, highlighting the high level of accuracy in our method’s ability to detect neurites, which is almost on par with manual analysis. This observation holds significance in analytical applications pertinent to Alzheimer’s disease.