Segmentation of astrocyte cells in fluorescently labeled images

Astrocytes, the most abundant brain cells, play crucial roles beyond mere support for neurons. Despite their significance, understanding them remains challenging due to imaging and analysis limitations. In this study, we developed a segmentation model that improves upon existing methods and conducted a comprehensive comparison of approaches from the literature, predominantly focusing on various U-net architecture variants. By optimizing configurations, loss functions, and processing steps, our U-net with Inception layers achieved an 83% F1 score, excelling in densely populated regions. The effectiveness of the developed segmentation model was assessed by applying it to images of astrocyte cells with different morphologies taken from serum-containing and serum-free cultures. By applying the Directional Ratio method to the segmented cells, it was determined that astrocytes in serum-free cultures exhibited greater anisotropy, thus demonstrating how serum influences astrocyte morphology through computational methods. Our findings emphasize the importance of multi-scale analysis in cell segmentation. The proposed method effectively segments and analyzes astrocyte images, revealing significant morphological changes influenced by serum, and offers a valuable tool for neuroscience studies by minimizing manual errors and enabling large-scale analysis.