We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation.
- Authors
Scherr, Tim; Seiffarth, Johannes; Wollenhaupt, Bastian; Neumann, Oliver; Schilling, Marcel P.; Kohlheyer, Dietrich; Scharr, Hanno; Nöh, Katharina; Mikut, Ralf
- 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.
- Subjects
DATA management; DEEP learning; SOFTWARE development tools; PHASE-contrast microscopy; GRAPHICAL user interfaces; PYTHON programming language
- Publication
PLoS ONE, 2022, Vol 17, Issue 11, p1
- ISSN
1932-6203
- Publication type
Article
- DOI
10.1371/journal.pone.0277601