Cell segmentation for immunofluorescence microscopy Covid-IF

Author

Constantin Pape

Description

This model segments cells imaged using fluorescence microscopy on anti-SARS-CoV2 antibodies in human sera. It predicts boundary maps and foreground probabilities. The boundaries can be processed e.g. with Cell Segmentation Recipe or Pixel classifier to obtain an instance segmentation (see also citation for segmentation algorithms)

• Input channel: 2D images, grayscale

• Scale: 1.24 um/pixel

• Bit depth: 8-bit

• Output channel: Channel 0, foreground probabilities, Channel 1: probability of boundary 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):

bioimageio-009-CovidIFCellSegmentationBoundary-105281_zenodo_5847355.zip

Download ZIP • 121.32MB

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 2022 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

Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera https://onlinelibrary.wiley.com/doi/10.1002/bies.202000257