Biomedical Image Processing with Containers and Deep Learning: An Automated Analysis Pipeline: Data architecture, artificial intelligence, automated processing, containerization, and clusters orchestration ease the transition from data acquisition to insights in medium‐to‐large datasets. (16th May 2019)

• Record Type: Journal Article
• Authors: González, Germán; Evans, Conor L.
• Is Part Of: BioEssays. Volume 41:Number 6(2019:Jun.)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-05-16
• Subjects: automation -- data processing -- image analysis -- optics; Molecular biology -- Periodicals; Cytology -- Periodicals; Developmental biology -- Periodicals; 572.8
• DOI: 10.1002/bies.201900004
• Languages: English
• ISSNs: 0265-9247

Abstract

Abstract : Here, a streamlined, scalable, laboratory approach is discussed that enables medium‐to‐large dataset analysis. The presented approach combines data management, artificial intelligence, containerization, cluster orchestration, and quality control in a unified analytic pipeline. The unique combination of these individual building blocks creates a new and powerful analysis approach that can readily be applied to medium‐to‐large datasets by researchers to accelerate the pace of research. The proposed framework is applied to a project that counts the number of plasmonic nanoparticles bound to peripheral blood mononuclear cells in dark‐field microscopy images. By using the techniques presented in this article, the images are automatically processed overnight, without user interaction, streamlining the path from experiment to conclusions. Abstract : High‐throughput acquisition devices leave researchers with a deluge of data. Automated processing is often a challenging task. Here a data processing framework is proposed based on deep learning, containerization, and orchestration for the analysis of medium‐to‐large datasets. The framework is applied to microscopy images of cells.