moco: Fast Motion Correction for Calcium Imaging
This enables faster analysis for closed-loop experiments in neuroscience, though it appears incremental as it builds on existing Fourier-transform approaches.
The researchers tackled the problem of slow motion correction in calcium imaging by developing a fast Fourier-transform algorithm that achieves comparable accuracy to established methods while being more stable to large translational motions.
Motion correction is the first in a pipeline of algorithms to analyze calcium imaging videos and extract biologically relevant information, for example the network structure of the neurons therein. Fast motion correction would be especially critical for closed-loop activity triggered stimulation experiments, where accurate detection and targeting of specific cells in necessary. Our algorithm uses a Fourier-transform approach, and its efficiency derives from a combination of judicious downsampling and the accelerated computation of many $L_2$ norms using dynamic programming and two-dimensional, fft-accelerated convolutions. Its accuracy is comparable to that of established community-used algorithms, and it is more stable to large translational motions. It is programmed in Java and is compatible with ImageJ.