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Cutting-edge deep learning techniques for bioimage analysis
DL4MicEverywhere / stardist-2d-zerocostdl4mic / 1.20.2

stardist-2d-zerocostdl4mic implementation.

2D instance segmentation of oval objects (ie nuclei). StarDist is a deep-learning method that can be used to segment cell nuclei in 2D (xy) single images or in stacks (xyz). Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
Tags
AMD64colabnotebookdenoisingZeroCostDL4Mic2Ddl4miceverywhere
Citation
https://doi.org/10.1038/s41467-021-22518-0von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0
https://doi.org/10.1007/978-3-030-00934-2_30Uwe Schmidt, Martin Weigert, Coleman Broaddus, Gene Myers. Cell Detection with Star-Convex Polygons. MICCAI 2018 (2018). https://doi.org/10.1007/978-3-030-00934-2_30
Solution written by
DL4MicEverywhere team
album team

Arguments

--path
What is your working path? (default value: .)

Usage instructions

Please follow this link for details on how to install and run this solution.