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Cutting-edge deep learning techniques for bioimage analysis
DL4MicEverywhere / u-net-3d-zerocostdl4mic / 2.2.1

u-net-3d-zerocostdl4mic implementation.

3D binary segmentation. The 3D U-Net was first introduced by Çiçek et al for learning dense volumetric segmentations from sparsely annotated ground-truth data building upon the original U-Net architecture by Ronneberger et al. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
Tags
AMD64colabnotebookdenoisingZeroCostDL4Mic2D
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
Özgün Çiçek, Ahmed Abdulkadir, Soeren S. Lienkamp, Thomas Brox, Olaf Ronneberger. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. https://arxiv.org/abs/1606.06650https://arxiv.org/abs/1606.06650
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.