Non-Invasive Single-Cell Morphometry in Living Bacterial Biofilms

Zhang, Ji; Wang, Yibo; Zhang, Mingxing; Wang, Jie; Achimovich, Alecia M.; Acton, Scott T.; Gahlmann, Andreas

doi:10.1016/j.bpj.2020.11.2216

Cited by 8 publications

(32 citation statements)

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“…studying filamentation, irregularly shaped cells or biofilms. In such cases, we refer to DL networks developed for the particular segmentation task (29,30,32,34,35). Instance segmentations can subsequently be used for downstream applications such as tracking cell lineages or morphological changes.…”

Section: Discussionmentioning

confidence: 99%

DeepBacs: Bacterial image analysis using open-source deep learning approaches

Spahn

Laine

Pereira

et al. 2021

Preprint

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Deep Learning (DL) is rapidly changing the field of microscopy, allowing for efficient analysis of complex data while often out-performing classical algorithms. This revolution has led to a significant effort to create user-friendly tools allowing biomedical researchers with little background in computer sciences to use this technology effectively. Thus far, these approaches have mainly focused on analysing microscopy images from eukaryotic samples and are still underused in microbiology. In this work, we demonstrate how to use a range of state-of-the-art artificial neural-networks particularly suited for the analysis of bacterial microscopy images, using our recently developed ZeroCostDL4Mic platform. We showcase different DL approaches for segmenting bright field and fluorescence images of different bacterial species, use object detection to classify different growth stages in time-lapse imaging data, and carry out DL-assisted phenotypic profiling of antibiotic-treated cells. To also demonstrate the DL capacity to enhance low-phototoxicity live-cell microscopy, we showcase how image denoising can allow researchers to attain high-fidelity data in faster and longer imaging. Finally, artificial labelling of cell membranes and predictions of super-resolution images allow for accurate mapping of cell shape and intracellular targets. To aid in the training of novice users, we provide a purposefully-built database of training and testing data, enabling bacteriologists to quickly explore how to analyse their data through DL. We hope this lays a fertile ground for the efficient application of DL in microbiology and fosters the creation of novel tools for bacterial cell biology and antibiotic research.

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Section: Discussionmentioning

confidence: 99%

DeepBacs: Bacterial image analysis using open-source deep learning approaches

Spahn

Laine

Pereira

et al. 2021

Preprint

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“…These results show that, when using BCM3D 2.0, ~86% of cells are segmented with physiologically reasonable cell shapes. The remaining 14% of cells can then either be excluded from the analyses or be to further processing to identify and correct the remaining segmentation errors 23,24,49 .…”

Section: Segmentation Of Experimentally Obtained Biofilm Imagesmentioning

confidence: 99%

BCM3D 2.0: Accurate segmentation of single bacterial cells in dense biofilms using computationally generated intermediate image representations

Zhang

Wang²,

Donarski³

et al. 2021

Preprint

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Accurate detection and segmentation of single cells in three-dimensional (3D) fluorescence time-lapse images is essential for measuring individual cell behaviors in large bacterial communities called biofilms. Recent progress in machine-learning-based image analysis is providing this capability with every increasing accuracy. Leveraging the capabilities of deep convolutional neural networks (CNNs), we recently developed bacterial cell morphometry in 3D (BCM3D), an integrated image analysis pipeline that combines deep learning with conventional image analysis to detect and segment single biofilm-dwelling cells in 3D fluorescence images. While the first release of BCM3D (BCM3D 1.0) achieved state-of-the-art 3D bacterial cell segmentation accuracies, low signal-to-background ratios (SBRs) and images of very dense biofilms remained challenging. Here, we present BCM3D 2.0 to address this challenge. BCM3D 2.0 is completely complementary to the approach utilized in BCM3D 1.0. Instead of training CNNs to perform voxel classification, we trained CNNs to translate 3D fluorescence images into intermediate 3D image representations that are, when combined appropriately later, more amenable to conventional mathematical image processing than a single experimental image. Using this approach, improved segmentation results are obtained even for very low SBRs and/or high cell density biofilm images. The improved cell segmentation accuracies in turn enable improved accuracies of tracking individual cells through 3D space and time, which opens the door to investigating time-dependent phenomena in bacterial biofilms at the cellular level.

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“…for tracking microspheres (Helgadottir, Argun and Volpe 2019 ) and bacterial cells (Zhang et al . 2020 ). It is worth noting however, that developing software to produce accurate and realistic training data resembling microscopy images is no trivial task and may initially be at least as time consuming as a manual annotation process.…”

Section: Introductionmentioning

confidence: 99%

Advances and opportunities in image analysis of bacterial cells and communities

Jeckel

Drescher

2020

FEMS Microbiology Reviews

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The cellular morphology and sub-cellular spatial structure critically influence the function of microbial cells. Similarly, the spatial arrangement of genotypes and phenotypes in microbial communities has important consequences for cooperation, competition, and community functions. Fluorescence microscopy techniques are widely used to measure spatial structure inside living cells and communities, which often results in large numbers of images that are difficult or impossible to analyze manually. The rapidly evolving progress in computational image analysis has recently enabled the quantification of a large number of properties of single cells and communities, based on traditional analysis techniques and convolutional neural networks. Here, we provide a brief introduction to core concepts of automated image processing, recent software tools, and how to validate image analysis results. We also discuss recent advances in image analysis of microbial cells and communities, and how these advances open up opportunities for quantitative studies of spatiotemporal processes in microbiology, based on image cytometry and adaptive microscope control.

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Non-Invasive Single-Cell Morphometry in Living Bacterial Biofilms

Cited by 8 publications

References 0 publications

DeepBacs: Bacterial image analysis using open-source deep learning approaches

DeepBacs: Bacterial image analysis using open-source deep learning approaches

BCM3D 2.0: Accurate segmentation of single bacterial cells in dense biofilms using computationally generated intermediate image representations

Advances and opportunities in image analysis of bacterial cells and communities

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