CSBDeep in Fiji

Deborah Schmidt edited this page Aug 3, 2018 · 3 revisions

This wiki page explains how you can setup Fiji/ImageJ to execute the example networks on different input data.

See the Installation Instructions for how to install and setup Fiji for the CSBDeep commands.

Fiji Commands

The plugin provides a set of CSBDeep model demonstrations which are located under Plugins > CSBDeep > Demo.

  1. Open an image, yours or one from our provided dataset
  2. Choose a suitable CSBDeep command
  3. Wait until all steps of the computation are completed and the result is displayed

You can also run your own network by using the plugin located under Plugins > CSBDeep > Run your network.

You can find more detailed instructions for individual commands below.

Important Notes

  1. Each CARE network should be trained with data for a specific combination of image content (e.g., nuclei, microtubules) and image corruption (camera noise, pixel size, microscope PSF, etc.). Hence, applying trained networks to images that are very dissimilar to the training data could lead to unexpected results. The pretrained networks provided via Fiji and KNIME are meant to showcase our method on the accompanying example data.
  2. Software for generating training data and training of CARE networks will be released soon. This will enable all users to employ CARE on their own image data.

3D Denoising (Tribolium)

See Fiji Command – 3D Denoising (Tribolium)

Input Output
3D grayscale image Prediction as a 3D grayscale image

3D Denoising (Planaria)

See Fiji Command – 3D Denoising (Planaria)

Input Output
3D grayscale image Prediction as a 3D grayscale image

Surface Projection (Flywing)

See Fiji Command – Surface Projection (Flywing)

Input Output
3D grayscale image Prediction as a 2D grayscale image

Isotropic Reconstruction (Retina)

See Fiji Command – Isotropic Reconstruction (Retina)

Input Output
3D image with 2 channels Prediction as a 3D image with 2 channels

Deconvolution (Microtubules)

See Fiji Command – Deconvolution (Microtubules)

Input Output
2D grayscale image or time series Prediction with the same dimensions
You can’t perform that action at this time.
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session.
Press h to open a hovercard with more details.