Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation

Abstract

Advances in microscopy hold great promise for allowing quantitative and precise readouts of morphological and molecular phenomena at the single cell level in bacteria. However, the potential of this approach is ultimately limited by the availability of methods to perform unbiased cell segmentation, defined as the ability to faithfully identify cells independent of their morphology or optical characteristics. In this study, we present a new algorithm, Omnipose, which accurately segments samples that present significant challenges to current algorithms, including mixed bacterial cultures, antibiotic-treated cells, and cells of extended or branched morphology. We show that Omnipose achieves generality and performance beyond leading algorithms and its predecessor, Cellpose, by virtue of unique neural network outputs such as the gradient of the distance field. Finally, we demonstrate the utility of Omnipose in the characterization of extreme morphological phenotypes that arise during interbacterial antagonism and on the segmentation of non-bacterial objects. Our results distinguish Omnipose as a uniquely powerful tool for answering diverse questions in bacterial cell biology.

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

“…The combination of the three components forms the platform ObiWan-Microbi , used to segment microbial organisms including E. coli, C. glutamicum and B. subtilis (SI S.3). For semi-automated segmentation, four pre-trained DLS algorithms are provided out-of-the-box and accessible in SegUI including Cellpose (Stringer et al ., 2021), Omnipose (Cutler et al ., 2021), Mask R-CNN trained on simulated image data (Sachs et al ., 2022) and Yolov5 for general object detection in images. Using semi-automated segmentation, an annotation speed of more than 200 cells/minute was achieved (SI S.4).…”

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

“…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 be subjected to further processing to identify and correct the remaining segmentation errors 23, 24, 49 and/or be subjected to further scrutiny to determine whether they are due to aberrant cell shapes exhibited by sick or intoxicated cells 55 .…”

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

“…12 Recent examples are Misic for the high-throughput cell segmentation of complex bacterial communities, 13 and Omnipose for robust segmentation of bacteria and other elongated cell shapes. 14…”

“…12 Recent examples are Misic for the high-throughput cell segmentation of complex bacterial communities, 13 and Omnipose for robust segmentation of bacteria and other elongated cell shapes. 14 In this paper, we present microbeSEG, a deep learning-based segmentation tool which uses the freely available OMERO 12 for data management. microbeSEG is a tool tailored to instance segmentation of various cell morphologies and imaging techniques.…”

“…Thus far, deep learning approaches for the segmentation of biological data either lack a smooth training data creation and retraining workflow, or a data management system like OMERO [14]. Recent examples are Misic for the high-throughput cell segmentation of complex bacterial communities [15], and Omnipose for robust segmentation of bacteria and other elongated cell shapes [13]. Fig 1 summarizes some typical barriers for end users when using cell segmentation software.…”