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

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.

A practical guide to bioimaging research data management in core facilities

“…Such structured data integrates well into image analysis pipelines, eased by the Application Programming Interfaces (APIs) of these tools. 107 Our experience with OMERO is that it eliminates the need to transfer data between collaborators, decreasing the total data volume and preventing divergences of processed data versions: the data stored centrally on a server is shared via links and accessed from different physical locations and software clients. Tools that are widely used by the community like QuPath, 108 ImageJ/Fiji, 99,100 or Python scripts within Jupyter Notebooks 109 and others have their dedicated OMERO clients.…”

“…This is why solutions such as OMERO or Cytomine are well suited to handle the complexity of microscopy data: researchers can benefit from the flexibility to view and organise images according to their preferences by leveraging the object‐oriented data organisation of such systems, and they can use annotations like tags, key‐value pairs (dictionaries) or tables of typed data, each with specific purposes. Such structured data integrates well into image analysis pipelines, eased by the Application Programming Interfaces (APIs) of these tools 107 …”