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:

• Training: train the CNN to recognize objects in 3D image stacks (or 2D images)

• Prediction: image segmentation

• Transfer Learning: adapt a pre-trained model to a new dataset or different object

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