PT  - JOURNAL ARTICLE
AU  - Tim Scherr
AU  - Johannes Seiffarth
AU  - Bastian Wollenhaupt
AU  - Oliver Neumann
AU  - Marcel P. Schilling
AU  - Dietrich Kohlheyer
AU  - Hanno Scharr
AU  - Katharina Nöh
AU  - Ralf Mikut
TI  - microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation
AID  - 10.1101/2022.04.29.489998
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.04.29.489998
4099  - http://biorxiv.org/content/early/2022/08/10/2022.04.29.489998.short
4100  - http://biorxiv.org/content/early/2022/08/10/2022.04.29.489998.full
AB  - 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 for 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 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 creation of training data to 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.Competing Interest StatementThe authors have declared no competing interest.