Mitochondria TOM20 artificial labeling - BioImage.io

harshyadav1
Dec 31, 2015
1 min read

Updated: Jul 29

Author

Estibaliz Gómez de Mariscal

Description

MitoNet: A conditional GAN (a.k.a. pix2pix) trained to infer mitochondrial label TOM20-Alexa Fluor 594 in HeLa cells from brightfield microscopy images. This model infers the corresponding 2D confocal microscopy image from a given brightfield image.

Input channel: 2D images, grayscale
Scale: 5-8 nm/pixel XY
Bit depth: 32-bit float
Output channels: 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.

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/.