Omnipose: a high-precision, morphology-independent solution for bacterial cell segmentation
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 context of characterizing extreme morphological phenotypes that arise during interbacterial antagonism. Our results distinguish Omnipose as a uniquely powerful tool for answering diverse questions in bacterial cell biology.
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Cell SegmentationVocal Bursts Intensity PredictionSimilar Papers 제목 키워드 기반
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