QUANTITATIVE ANALYSIS OF FLUORESCENT IMAGES OF GLIA CELLS USING DEEP NEURAL NETWORKS
The human brain is an incredibly intricate system comprising not only neurons but also another diverse group of cells known as glia. In recent years, there has been a remarkable surge in interest among neuroscientists regarding glia cells due to their crucial role in brain function. Among the various glia cell types, astrocytes, the most abundant, actively participate in numerous aspects of brain physiology, while microglia, a different subgroup of glia, serve as the brain's immune cells, protecting against infection and inflammation. Both astrocytes and microglia exhibit significant heterogeneity and complex morphological properties, which pose a considerable challenge for rigorous quantitative analysis. To address this challenge, my doctoral research aimed to develop a new class of computational methods that are accurate and efficient for the quantitative analysis of glia cells. To achieve this objective, I devised algorithms for the precise detection of astrocytes, microglia, and potentially other glia subfamilies in microscopy images of brain tissue. A notable innovation in my approach was the utilization of YOLO, an advanced deep learning platform for object detection, which I optimized to yield highly efficient cell detection models. Through extensive numerical experiments using multiple image datasets, I demonstrated that this method performs competitively compared to both conventional and state-of-the-art techniques, even in scenarios where cell density is high. Additionally, leveraging the outcomes of my glia detection pipeline, I developed an innovative method for the morphological analysis of astrocytes and microglia aimed to identify potential biological biomarkers in images of spinal cord injury.