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Mitochondria EM Segmentation SEM - BioImage.io

Updated: May 7





Author

Abhishek Bhardwaj, Kedar Narayan


Description

MitoNet: A generalist mitochondrial instance segmentation model trained using for EM images in 2D.


  • Input channel: 2D images, grayscale

  • Scale: 5-8 nm/pixel XY

  • Bit depth: 8-bit

  • Output channel: Channel 0, foreground probabilities


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


Download

By downloading, installing, copying, accessing, or using the software, you agree to the terms of this end user license agreement.


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.

  1. 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.

  2. Load the test image (or any image of your own) into Aivia.

  3. 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.

  4. Click 'Start' to apply the model.


License

Copyright 2023 Bhardwaj & Narayan, BSD 3.0. Full license information can be found here.


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.



References

 
 
 

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