Activity recognition in live-cell imaging is labor-intensive and requires significant human effort. Existing automated analysis tools are largely limited in versatility. We present the intelligent vesicle exocytosis analysis (IVEA) platform, an ImageJ plugin for automated, reliable analysis of virtually any fluorescence-labelled vesicle fusion events and other burst-like activity such as calcium sparks. IVEA functions as a plugin within the ImageJ environment, utilizing deep learning techniques. It is a modularly structured tool that was trained on live cell data collected to measure synaptic transmission in neurons, lytic granule release in cytotoxic T cells, and dense core release in INS1 cells and in chromaffin cells. To increase its versatility, we included data generated with different fluorophores, including pH-sensitive and insensitive probes, which exhibit different fluorescence signal decay patterns after fusion. Finally, we developed a training software that enables us to quickly refine existing models using minimal training data. Using this feature, IVEA successfully detected calcium sparks in cardiomyocytes, demonstrating its ability to detect and classify any burst-like event. The IVEA is 60 times faster than manual analysis, it performs batch analysis and can detect rare events that are often overlooked by humans, representing a significant advancement in exocytosis image analysis. IVEA’s versatility can be expanded by refining or training new models via an integrated interface. With its impressive speed and remarkable accuracy, IVEA represents a seminal advancement in exocytosis image analysis and other burst-like fluorescence fluctuations applicable to a wide range of microscope types and fluorescent dyes.
Chouaib et al. (Sun,) studied this question.