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.

Fig 1. Typical barriers when using segmentation software.

Cell segmentation methods are typically designed for specific applications and therefore often lack a data management system with a versatile data importer. This shortcoming results in the need for file format and image shape conversion steps. Furthermore, multiple tools often need to be combined to cover the whole workflow, from training data creation to applying trained models. Again, further processing steps may be required to enable tool compatibility. For many applications, it is not (yet) possible to do without own annotated training data. Interactive cropping functionalities are helpful in this case and enable an efficient annotation for dense-growing organisms. Nevertheless, this feature has not yet been included in cell segmentation software. Note: fluorescence images have been inverted for Omnipose [13], and phase contrast images have been inverted for Cellpose [14] for better results.

Fig 2. microbeSEG overview.

OMERO is used for data management since it provides a versatile data importer and standardizes file handling [15]. Data can be viewed in the browser with the OMERO.web client. microbeSEG offers training data creation, training, evaluation, and inference functionalities. The jointly developed toolkit ObiWan-Microbi is used for manual annotation and result correction [18].

Fig 3. Crop creation interface.

Automatically proposed crops can be selected and uploaded to OMERO (a). The crop proposals are extracted randomly from different non-overlapping image regions (the left crop originates from the left image region, and the right crop from the right). For the pre-labeling, it is possible to upload only the image or the image with its prediction (b). The image-only upload is helpful when the pre-label predictions require too many manual corrections.

Fig 4. Training data representations of the two microbeSEG methods.

From the ground truth (a—instances: color-coded), the boundary representation (b—cell interior: gray, cell boundary: white) and the distance representations (c—distance to background, inverse distance to neighbors) can be computed. A deep learning model is trained to predict either the boundary representation or the distance representations, and the single instances are recovered in the post-processing.

Fig 5. Exemplary images and segmentation overlays for B. subtilis and E.coli.

Shown are the results of the median distance method microbeSEG models from Table 1. S2 Fig shows the raw network outputs of the microbeSEG model (d) and results for the boundary method.

Fig 6. Segmentation of U2OS cells from the BBBC039 dataset [32].

The microbeSEG segmentation model (default settings) has been trained on HeLa cells from the Cell Tracking Challenge [33, 34].

Fig 7. Exemplary microbeSEG segmentation (a) and analysis results (b) for a growing C. glutamicum colony.

The results can easily be viewed in Fiji. S1 Video shows a video of the segmentation results of the growing colony.