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CDeep3M2 overview:

  • provides a plug-and-play deep learning solution for large-scale image segmentation of light, electron and X-ray microscopy.
  • is distributed as cloud formation template for AWS cloud instances, as docker container and as singularity container for local installs or supercomputer clusters.
  • is backwards compatible, allowing users to continue using models that have been trained with earlier versions of CDeep3M.
  • compared to v1.6.3 provides improvements in speed for larger datasets
  • facilitates additional augmentation strategies (secondary: noise additions, denoising, contrast modifications; tertiary: re-sizing)
  • facilitates providing multiple training volumes to train broadly tuned models.
  • provides enhanced robustness using automated image enhancements.
  • code implemented in Python 3.
  • Generates automatically enhanced images and an overlay of the segmentation with the enhanced images for visual verification.

Running CDeep3M2

Use Description Link Documentation
CDeep3M2-Preview: Extremely quick tests, fully automated instantaneous runs Link Documentation
CDeep3M2-Docker: Local or remote, large runs, long trainings, simple installation, GPU with min 12GB vRAM required Link Documentation
CDeep3M2-AWS: Remote, large runs, long trainings, simple installation, pay for GPU/hour (entry level 0.50$/h) Link Documentation
CDeep3M2-Colab: Remote, short runs or re-training, simple installation, free GPU access Link Documentation
CDeep3M2-Singularity: Local or cluster, large runs, long trainings, often required for compute cluster Link Documentation

Steps to process images with CDeep3M2:

For prediction and transfer learning you can use either a pre-trained model from the modelzoo or your own model generated during training.

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Plug-and-Play deep learning for image segmentation of large-scale microscopy data

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