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Pancreatic phase contrast cell segmentation -


Ignacio Arganda Carreras


U-Net trained to segment phase contrast microscopy images of pancreatic stem cells on a 2D polystyrene substrate. [1]

  • Input channel: phase contrast grayscale image.

  • Scale: 1.6 um/pixel.

  • Bit depth: 8 bit.

  • Output channel: cell probability.

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


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


Make sure you have installed Aivia and the required DeepLearning module (which includes Python 3.9.7 and CUDA 11).

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.


Copyright 2021 Ignacio Arganda Carreras. Additional license information can be found here.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

  1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

  2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

View license available online: BSD-2-clause

Acknowledgement is supported by AI4Life. AI4Life has received funding from the European Union's Horizon Europe research and innovation programme under grand agreement number 101057970.


  1. F Lux and P Metula. Cell segmentation by combining marker-controlled watershed and deep learning. 2020. ArXiv.

  2. E Gómez-de-Mariscal, et al. DeepImageJ: a user-friendly environment to run deep learning models in ImageJ. Nature Methods. 2021. 18: 1192-95. [doi]

  3. V Ulman, et al. An objective comparison of cell-tracking algorithms. Nature Methods. 2017. 14: 1141-52. [doi]

  4. O Ronneberger, et al. U-Net: convolutional networks for biomedical image segmentation. 2015. ArXiv.


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