ObiWan-Microbi: OMERO-based integrated workflow for annotating microbes in the cloud

Abstract

Automated microscopy techniques combined with high-throughput microfluidic cultivation systems provide unique insights into living microorganisms, enabling hundreds of experiments to be performed in parallel. Such setups generate large data sets with rich cellular features that need to be extracted to arrive at quantitative insights. The sheer amount of recorded time-lapse images requires reliable automated processing. While recent advances in deep learning methods have enabled automated processing, these methods rely on large-scale and precisely annotated data sets, often not available for new organisms or custom imaging modalities.To overcome the annotated data bottleneck, particularly in the microbial domain, we present the open-source ObiWan-Microbi platform providing a fast workflow for large-scale annotation of up to hundred thousands of segmentation and tracking annotations in time-lapse imaging data. ObiWan-Microbi focuses on easy-to-use semi-automated annotation in the browser eliminating the need for local installation or accelerator hardware, encourages FAIR data management using OMERO, and provides convenient collaborative cloud deployment to simplify the creation and long-term development of large-scale annotated data sets. The public availability of such benchmark data sets has the potential to improve data-driven methods, increase comparability among them, and is an essential step towards reliable automated image processing in microbial live-cell microscopy.

Keywords

• Microbial live-cell analysis
• Cell annotation
• Segmentation
• Tracking
• Microservice architecture
• OMERO