Work

Biomedical Image Processing with Containers and Deep Learning: An Automated Analysis Pipeline

Year: 2019

Type: review

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.

Source: BioEssays

DOI: 10.1002/bies.201900004

Authors: Germán González, Conor L. Evans

Institutions: DNP Scientific Analysis Center, Inc. (Japan), Harvard University, Massachusetts General Hospital

Language: English

Topic: Cell Image Analysis Techniques

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $3,800

Funders: Foundation for the National Institutes of Health, Nvidia, National Cancer Institute