microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation

PLoS ONE, vol. 17, pp. e0277601

Abstract

In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell level. Therefore, we present microbeSEG, a user-friendly Python-based cell segmentation tool with a graphical user interface and OMERO data management. microbeSEG utilizes a state-of-the-art deep learning-based segmentation method and can be used for instance segmentation of a wide range of cell morphologies and imaging techniques, e.g., phase contrast or fluorescence microscopy. The main focus of microbeSEG is a comprehensible, easy, efficient, and complete workflow from the creation of training data to the final application of the trained segmentation model. We demonstrate that accurate cell segmentation results can be obtained within 45 minutes of user time. Utilizing public segmentation datasets or pre-labeling further accelerates the microbeSEG workflow. This opens the door for accurate and efficient data analysis of microbial cultures.

Authors 9

1. Tim Scherr corresponding

   Karlsruhe Institute of Technology

   Affiliation as printed

   Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany

2. Johannes Seiffarth Aachen Computational Systems Biology (AVT.CSB)

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Computational Systems Biology (AVT.CSB), RWTH Aachen University, Aachen, Germany

   Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany

3. Bastian Wollenhaupt

   Forschungszentrum Jülich

   Affiliation as printed

   Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany

4. Oliver Neumann

   Karlsruhe Institute of Technology

   Affiliation as printed

   Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany

5. Marcel P. Schilling

   Karlsruhe Institute of Technology

   Affiliation as printed

   Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany

6. Dietrich Kohlheyer

   Forschungszentrum Jülich

   Affiliation as printed

   Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany

7. Hanno Scharr

   Forschungszentrum Jülich

   Affiliation as printed

   Institute for Advanced Simulation, IAS-8: Data Analytics and Machine Learning, Forschungszentrum Jülich GmbH, Jülich, Germany

   Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Jülich, Germany

8. Katharina Nöh corresponding

   Forschungszentrum Jülich

   Affiliation as printed

   Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany

9. Ralf Mikut corresponding

   Karlsruhe Institute of Technology

   Affiliation as printed

   Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany