A scalable data management framework for image flow cytometry research using OMERO

Recent advances in flow cytometry have incorporated imaging capabilities, enabling the high-throughput analysis of cellular morphology and subcellular structures.
The integration of machine learning and artificial intelligence algorithms has further enhanced data interpretation, facilitating automated classification and feature extraction. The large-scale image and metadata information generated by this approach present significant challenges, including substantial storage requirements, standardization issues, and computational demands for processing high-dimensional information.