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Expansion - Microtubules

Updated: Nov 18, 2020

This model enhances the spatial resolution of volumetric fluorescence microscopy images to simulate expansion microscopy (ExM) data. The model is well suited for enhancing spatial resolution of live cell imaging - which is incompatible with ExM. This model is trained using the 3D-RCAN architecture, developed in-house (read about it on BioRxiv [1]).

Post-expansion 3D volumetric images of fluorescently-labeled microtubules are captured on an instant structured illumination microscopy (iSIM) system with a 60x 1.2 NA water immersion objective. DAPI stain is used for estimating the expansion factor, which is 4x for the dataset. The post-expansion data is blurred with a modified PSF matched to the expansion factor with noise added to simulate pre-expansion data as input. The model is trained using image pairs of simulated input and post-expansion data.



Make sure you have Python versions 3.5 to 3.7 installed with TensorFlow-GPU version 1.13.0 and above.

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.


  1. J Chen, et. al. Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes. BioRxiv.