I spent some time this morning testing the new CARE deep learning plug in.
The purpose of my testing is to try and understand how the deconvolution network for Microtubules is working, to try and understand how it would deal with artifacts (such as crosstalk and noise) and gain some experience with the plugin. The tests do not reflect how one should use it for real analysis. For real analysis you must take care that the network was trained on data similar to the data you are processing.
I tested the plugin on all three channels of the Hela-Cells sample image. Even though only the Green channel contains microtubules. I was interested in how the network would perform on structures it wasn’t trained for.
Even though the green channel of Hela-Cells contains microtubules, it was acquired with a different scope and settings than the training set so performance will not be optimal.
The plugin currently does not output the “control” image. The control image contains information about uncertainty. A complete analysis would need to take into account the uncertainty of the results.
To start with I’ve been testing the “Deconvolution - Microtubules” and comparing the results to (relatively simple) Gaussian deconvolution.
I used the Hela-Cells sample image cropped using
I’ll present the results for each channel in separate replies.