A* Path-Finding Algorithm to Determine Cell Connections

<p dir="ltr">This study presents a novel computational approach to analyzing astrocyte reactivity following laser ablation-induced brain injury. Traditionally, astrocyte responses were manually assessed by measuring calcium levels and classifying cells based on their proximity to the ablated site—categorized as disconnected, networked, or connected. However, manual annotation proved inefficient and error-prone. To address this, the research integrates a modified A* pathfinding algorithm with a U-Net convolutional neural network, a custom statistical binary classification method, and a personalized Min-Max connectivity threshold to automate the detection of astrocyte connectivity.</p><p dir="ltr">Astrocytes were dissociated from E18 mouse cortical tissue, and image data were processed using a Cellpose 2.0 model to mask nuclei. Pixel paths were classified using a z-score brightness threshold of 1.21, optimized for noise reduction and accuracy. The A* algorithm then evaluated connectivity by minimizing Euclidean distance and heuristic cost between cells. Connections were labeled as disconnected, networked, or connected based on path existence and threshold criteria.</p><p dir="ltr">This automated pipeline achieved 94% accuracy and completed analysis in under 10 seconds—over 200 times faster than manual methods. The integration of heuristic optimization and machine learning significantly enhances both speed and precision in astrocyte data analysis. Future work aims to generalize this algorithm for broader biological applications by training additional Cellpose models and adapting the A* framework.</p>