Nucleus segmentation for fluorescence microscopy - BioImage.io
- jose-miguelserra-l
- Jan 1, 2016
- 2 min read
Updated: May 6

Author
Constantin Pape
Description
This model segments nuclei in fluorescence microscopy images. It predicts boundary maps and foreground probabilities for nucleus segmentation in different light microscopy modalities, mainly with DAPI staining.
Input channel: 2D images, grayscale
Scale: 3 um/pixel
Bit depth: 8-bit
Output channel: Channel 0, probability of nuclei, Channel 1: probability of nuclei contour
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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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
Copyright 2023 Constantin Pape, CC-BY-4.0. 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.
About AI4Life: https://ai4life.eurobioimaging.eu/.
References
Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl
Segmentation algorithm: https://doi.org/10.1038/nmeth.4151
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