Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17222908

Author: Massei, Riccardo

Institution: Helmholtz Centre for Environmental Research

Language: English

Topic: Cell Image Analysis Techniques

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funder: Deutsche Forschungsgemeinschaft

Award: NFDI4BIOIMAGE Nationale Forschungsdateninfrastruktur für Mikroskopie und Bildanalyse