Quantitative microscopy techniques such as super-resolution imaging are made possible by computational analysis. Our open-source platforms are designed to improve live-cell and super-resolution microscopy and to assist researchers in obtaining accurate data. We created the NanoJ ecosystem specifically for super-resolution microscopy and analysis. In this presentation, I will introduce our latest method, “enhanced Super-Resolution Radial Fluctuations” (eSRRF), and its integration with real-time controlled microfluidics live-to-fix cell imaging, known as NanoJ-Fluidics. I will also discuss the application of real-time quality control on predicted super-resolution images using SQUIRREL. Furthermore, I will introduce ZeroCostDL4Mic, a platform that utilizes free cloud computing for Deep Learning analysis of biological images, enabling segmentation and detection without coding expertise. New tools such as NanoPyx find application in studying eukaryotic and prokaryotic cells and in single-molecule localization microscopy data analysis.<\/p>\n
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