CebraNET Cellular Membranes for SEM - BioImage.io

jose-miguelserra-l
Jan 1, 2016
2 min read

Updated: May 7

Author

Julian Hennies

Description

Cellular membrane prediction model for volume SEM datasets. This model was trained on a FIB-SEM dataset to generically predict membranes (or organelle boundaries) in any volume SEM dataset. It predicts cell membrane maps. The boundaries can be processed with other tools to achieve segmentation (see citation).

Input channel: 3D images, grayscale
Scale: 4 nm/pixel isotropic
Bit depth: 8-bit
Output channel: probability of cell membrane maps

This model was downloaded and converted from BioImage.io, respecting the associated license (see License section below for more information)

Download

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Model file (.aiviadl)
Test image (.tif)

Download model file and test image (ZIP archive)

Requirements

Make sure you have installed Aivia and the required DeepLearning module (according to our Wiki).

Installation and apply instructions

Aivia is required for applying the model file. You can request a demo copy of Aivia here.

Drag-and-drop the model file into the Recipe Console area; or use the 'Load recipe' option in the Recipe Console to load the model file.
Load the test image (or any image of your own) into Aivia.
If your image contains more than one channel, click on the 'Input & Output' section and specify the image channel you wish to apply the model on.
Click 'Start' to apply the model.

License

Acknowledgement

Bioimage.io is supported by AI4Life. AI4Life has received funding from the European Union's Horizon Europe research and innovation program under grant agreement number 101057970.

About AI4Life: https://ai4life.eurobioimaging.eu/.