scipion-em-cryocare 4.2.4

Scipion plugin for cryoCARE

This plugin allows to use cryoCARE -trains a denoising U-Net for tomographic reconstruction according to the Noise2Noise training paradigm- tomography methods into Scipion framework.

Installation

The plugin can be installed in user (stable) or developer (latest, may be unstable) mode:

1. User (stable) version::

scipion3 installp -p scipion-em-cryocare

2. Developer (latest, may be unstable) version::

• Clone the source code repository:

git clone https://github.com/scipion-em/scipion-em-cryocare.git

• Install:

scipion3 installp -p local/path/to/scipion-em-cryocare --devel

Protocols

The integrated protocols are:

1. Load a previously trained model.

2. Generate the training data.

3. Training: uses two data-independent reconstructed tomograms to train a 3D cryoCARE network.

4. Predict: generates the final restored tomogram by applying the cryoCARE trained network to both even/odd tomograms followed by per-pixel averaging.

Tests

The installation can be checked out running some tests. To list all of them, execute:

scipion3 tests --grep cryocare

To run all of them, execute:

scipion3 tests --grep cryocare --run

Tutorial

The test generates a cryoCARE workflow that can be used as a guide about how to use cryoCARE. The even/odd tomograms required to use cryoCARE can be generated inside Scipion with:

1. Plugin scipion-em-motioncorr: protocol “align tilt-series movies”.

2. Plugin scipion-em-xmipptomo: protocol “tilt-series flexalign”.

References

• Cryo-CARE: Content-Aware Image Restoration for Cryo-Transmission Electron Microscopy Data. Tim-Oliver Buchholz et al., 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

Contact information

If you experiment any problem, please contact us here: scipion-users@lists.sourceforge.net or open an issue.

We’ll be pleased to help.

Scipion Team