Poor outcome of PW-Rigid Motion Correction on 2P Data

[mohammadsohaib]

Your setup:

1. Operating System (Linux, MacOS, Windows): Linux
2. Hardware type (x86, ARM..) and RAM: x86 , 64gb
3. Python Version (e.g. 3.9): 3.11
4. Caiman version (e.g. 1.9.12): 1.11.2
5. Which demo exhibits the problem (if applicable):
6. How you installed Caiman (pure conda, conda + compile, colab, ..): Custom installation (conda + compile)
7. Details:
   Issue Description:
   I am encountering issues while running motion correction on my 2P imaging data using the pw-rigid method, as the results look worse than the original data. Previously, I attempted rigid correction, but it failed to produce satisfactory results, so I switched to pw-rigid. I have experimented with several parameter variations in pw-rigid, but the outcome remains suboptimal.

For validation, I have been analyzing both the maximum projection and correlation images as suggested in the demo. However, the motion-corrected images appear significantly degraded when compared to the original data.

I have attached a figure comparing the original and motion-corrected images, as well as a demo movie for reference.

Parameters Used:
PW-Rigid parameters:

Dataset-dependent parameters: Objective Zoom (1.5x) 20X lens, frame rate: 30, frame size: 512X512
I would appreciate any insights or suggestions on what might be causing this issue, and how to improve the motion correction process.

Thank you for your assistance.

Link to the demo video: https://drive.google.com/file/d/1dC-sA7IWZwXLmfKHU2fP_mGZSqNFBUQN/view?usp=drive_link

[pgunn]

Thanks for providing a download link; we'll give it a look. I'm hoping this is something we can fix with different parameters.

[kushalkolar]

those strides look way too huge

mesmerize-core can help you determine optimal params in a more structured way: https://github.com/nel-lab/mesmerize-core

[mohammadsohaib]

@kushalkolar Thanks for the suggestion, I will look into mesmerise as well.
I did start with a small stride value eg:48 and then increased it.