abstract

Functional 2-photon microscopy is a key technology for imaging neuronal activity. The recorded image sequences, however, can contain non-rigid movement artifacts which requires high-accuracy movement correction. Variational optical flow (OF) estimation is a group of methods for motion analysis with established performance in many computer vision areas. However, it has yet to be adapted to the statistics of 2-photon neuroimaging data. In this work, we present the motion compensation method Flow-Registration that outperforms previous alignment tools and allows to align and reconstruct even low signal-to-noise ratio 2-photon imaging data and is able to compensate high-divergence displacements during local drug injections. The method is based on statistics of such data and integrates previous advances in variational OF estimation. Our method is available as an easy-to-use ImageJ / FIJI plugin as well as a MATLAB toolbox with modular, object oriented file IO, native multichannel support and compatibility with existing 2-photon imaging suites.

Video Results

Supplemental video 1 (drug injection sequence)




Supplemental video 2 (saline injection sequences)




Supplemental video 3 (layer23 - layer1 - layer5 - CaPKA)




Supplemental video 4 (Jupiter demo)




Code and Citation

Source

The Flow-Registration toolbox is available on GitHub.

Paper

Our paper is available in Journal of Biophotonics.

Please cite our work with:

P. Flotho, S. Nomura, B. Kuhn, and D. J. Strauss, “Software for Non-Parametric Image Registration of 2-Photon Imaging Data,” J Biophotonics, 2022. 

@article{flotea2022a,
    author = {Flotho, P. and Nomura, S. and Kuhn, B. and Strauss, D. J.},
    title = {Software for Non-Parametric Image Registration of 2-Photon Imaging Data},
    journal = {J Biophotonics},
    pages = {e202100330},
    doi = {10.1002/jbio.202100330},
    eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/jbio.202100330},
    year={2022},
    note = {e202100330 jbio.202100330.R2}
}