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 for analyzing acquired data is accurate and automated cell segmentation at the single-cell level. Therefore, we present microbeSEG, a user-friendly cell segmentation tool with 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 data sets or pre-labeling further accelerates the microbeSEG workflow. This opens the door for accurate and efficient data analysis of microbial cultures.

Software Tools for 2D Cell Segmentation

“…Most of the traditional segmentation methods are based on the intensity and spatial relationship of pixels, and the constraint model is found by manual optimization, requiring expertise in basic techniques including code adaptation [12]. Code adaptation is highly subjective, and its development has reached a bottleneck.…”

“…They learn from data, adapt to different problem settings, and leverage the capabilities of pre-trained models, making them a convenient and effective strategy in many research and application fields. Further, manual tuning is not needed; however, retraining with annotated data is required [12].…”