Updated: Dec 6, 2022
This model enhances the spatial resolution of confocal image volumes to that of stimulated emission depletion (STED) microscopy. This model is trained using the 3D-RCAN architecture, developed in-house (read about it on BioRxiv ).
The model is trained on aligned 3D volumetric image pairs of fixed mouse embryonic fibroblast (MEF) cells with nuclear pore complexes (NPC) labeled with Alexa Fluor 594. The image pairs were acquired on a Leica SP8 3X system using a 100x 1.4 NA objective operating in confocal (input) and STED (ground truth) modes.
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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.
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.
J Chen, et. al. Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes. BioRxiv.