Computational Strategies for STED Microscopy and Applications-AI Insights
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STED microscopy's power is amplified by computational strategies, crucial for overcoming its limitations like photobleaching and noise. Machine learning, especially deep learning, enhances image quality, revealing hidden details in raw data. These tools analyze large datasets to discern patterns and predict obscured structures, enabling deeper understanding of biological processes. Computational methods also optimize experiments, reducing exposure and artifacts, thus preserving sample integrity. Automation and quantitative analyses streamline research, allowing focus on biological questions. The synergy of STED and computation is driving nanoscale imaging, unlocking new discoveries in various scientific fields.