Automated Image Analysis for Multimodal Optical Microscopy

Optical microscopy is essential for visualizing cellular processes and tackling challenges in biology and medicine; however, the vast amount of image data generated makes manual analysis labor-intensive and inconsistent. To address these challenges, we have developed advanced microscopy image analysis tools for fluorescence, dark field, and phase contrast imaging, integrating novel algorithms and neural networks to achieve efficient automated statistical analysis. Fluorescence imaging captures light emitted by excited fluorophores, dark field imaging enhances contrast by collecting scattered light against a dark background, and phase contrast imaging reveals transparent structures based on phase shifts in transmitted light. Our approach employs dynamic thresholding and morphology-based analysis for automated object detection, while convolutional neural networks (CNNs) identify specific cellular structures. Recurrent neural networks (RNNs) were used to monitor dynamic cellular changes and classify biological processes with over 90% accuracy. These techniques were applied to cancer cell images for drug screening applications and sperm cell images for fertility testing. This AI driven microscopy analysis significantly enhances precision and throughput.