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B. Subtilis bacteria segmentation -

Updated: Aug 9, 2023


Estibaliz Gómez de Mariscal


This trained 2D U-Net model segments the contour, foreground and background of Bacillus Subtilis bacteria imaged with Widefield microscopy images.

  • Input channel: Bacteria image.

  • Scale: 100x.

  • Bit depth: 8 bit.

  • Output channels: background probability, foreground probability and contour probability of bacteria.

This model was downloaded and converted from, respecting the associated license (see the 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 2023 Estibaliz Gómez de Mariscal. Full license information can be found here.

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. Falk et al. U-Net: deep learning for cell counting, detection and morphometry. Nature Methods. 2019:16: 67-70. [doi]

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

  3. L von Chamier et al. ZeroCostDL4MIC: an open platform to use deep-learning in microscopy. 2020. BioRxiv.

  4. C Spahn et al. DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches. Communications biology. 2022. 5: 688. [doi]


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