Microscopy Image Analysis

Microscopy image analysis turns images from biological and materials microscopes into measurements, such as cell counts, tissue structure, particle properties, and cell motion. Common approaches include classical image processing and deep learning for segmentation, detection, classification, tracking, and 2D or 3D reconstruction; newer work also uses self-supervised and foundation models, physics-informed methods, synthetic data, and expert interaction. These tools can make analysis more scalable and reproducible, but performance often depends on microscope settings, sample type, image quality, and the amount of expert annotation available. A central challenge is establishing reliable generalization and evaluation: strong benchmark scores may not ensure robust measurements on new acquisitions, and visual quality metrics may miss errors that affect downstream scientific conclusions.