Software for non‐parametric image registration of 2‐photon imaging data - Flotho, Philipp; Nomura, Shinobu; Kuhn, Bernd; Strauss, Daniel J. (Journal of biophotonics, 2022)
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 multi‐channel support and compatibility with existing 2‐photon imaging suites. We present the non‐parametric, high‐accuracy and fast motion compensation method Flow‐Registration that can compensate challenging 2‐Photon neuroimaging videos which are contaminated with large and or high divergence displacements. Our method is publicly available as a MATLAB toolbox and ImageJ/FIJI plugin on GitHub: https://github.com/phflot/flow_registration