Package: misic-napari
misic-napariA napari plugin forMiSiC. Segmentation of bacteria in dense colonies.
The plugin provides acces to preprocessing of the image like scaling, gamma correction, sharpness and noise variance that can improve the segmentation of bacteria irrespective of the imaging modality.Install NapariInstall napari either the bundled app or through [pip/conda]https://napari.org/#installationInstallationInstallmisic-naparithrough plugin manager in napari.OrYou can installmisic-naparivia [pip] in the napari console:pip install misic-napariTutorialNote:
The image should be in the format [n,row,col] or [row,col], i.e., a single image or a stack. Hyper-stacks are not supported yet.get_widthCreates a Shapes layer with name 'cell-width' where the cell width can be hand drawn using line drawing tools in the shapes layer. This need not be precise and can be adjusted later. Clickget_cell_widthto obtain the desired mean cell width. This will be used to scale the image accordingly before segmentation.segmentThis can be used to quickly set the parameters that can be later used to segment the whole stack.use_roiA square ROI of side 256 is created by default for quickly checking adjusting the segmentation parameters. The roi can be resized or moved in theroishapes layer.light_backgroundTrue; for phase-contrast images.False; for bright-field and fluorescence images.use_local_noiseIf checked, this adds noise to image with local variance. In this case, a noise_var of around 0.1 works well. If unchecked, this adds noise with global variance of noise_var/100. Adding may help in removing false positives.gaussian_laplaceUseful when segmenting fluorescence images.adjust_scaleFine-tuning the scale around ([0.8,1.2]) the scale obtained from cell-width determined inget_cell_width.noise_varAmount of noise to be added to the image at the preprocessing step. This helps reduce the False Positives and, in many cases, to separate cells effectively.gammagamma correctionsharpness_scale and sharpness_amountUnsharp mask based sharpness with sigma = sharpness_scale and amount = sharpness_amountsegment_stackSegments the entire stack using the parameters that were obtained in "segment".saveThe parameters can be saved in a json file.LicenseDistributed under the terms of the [MIT] license,
"misic-napari" is free and open source softwareCite@article {10.7554/eLife.65151,
article_type = {journal},
title = {Misic, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities},
author = {Panigrahi, Swapnesh and Murat, Dorothée and Le Gall, Antoine and Martineau, Eugénie and Goldlust, Kelly and Fiche, Jean-Bernard and Rombouts, Sara and Nöllmann, Marcelo and Espinosa, Leon and Mignot, Tâm},
editor = {Xiao, Jie and Storz, Gisela and Hensel, Zach},
volume = 10,
year = 2021,
month = {sep},
pub_date = {2021-09-09},
pages = {e65151},
citation = {eLife 2021;10:e65151},
doi = {10.7554/eLife.65151},
url = {https://doi.org/10.7554/eLife.65151},
abstract = {Studies of bacterial communities, biofilms and microbiomes, are multiplying due to their impact on health and ecology. Live imaging of microbial communities requires new tools for the robust identification of bacterial cells in dense and often inter-species populations, sometimes over very large scales. Here, we developed MiSiC, a general deep-learning-based 2D segmentation method that automatically segments single bacteria in complex images of interacting bacterial communities with very little parameter adjustment, independent of the microscopy settings and imaging modality. Using a bacterial predator-prey interaction model, we demonstrate that MiSiC enables the analysis of interspecies interactions, resolving processes at subcellular scales and discriminating between species in millimeter size datasets. The simple implementation of MiSiC and the relatively low need in computing power make its use broadly accessible to fields interested in bacterial interactions and cell biology.},
keywords = {Deep learning, image analysis, microscopy, myxococcus xanthus, biofilms},
journal = {eLife},
issn = {2050-084X},
publisher = {eLife Sciences Publications, Ltd},
}