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Matlab routines for online non-rigid motion correction of calcium imaging data
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@MotionCorrection class MotionCorrection Nov 11, 2017
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NoRMCorreSetParms.m
README.md
apply_shifts.m
bigread2.m
cell2mat_ov.m moving files Dec 2, 2016
cell2mat_ov_sum.m dealing with NaNs Dec 8, 2016
concatenate_files.m remove subdir dependency from concatenate_files Jul 24, 2018
construct_grid.m
construct_grid_even.m
construct_weights.m
correct_bidirectional_offset.m clip when correcting offset due to bidirectional scanning Oct 24, 2017
demo.m
demo_1p.m
demo_1p_low_RAM.m fixed bug when reading avi files in chunks Apr 4, 2018
demo_mc_class.m
dftregistration_min_max.m rigid reg batch_even Jun 4, 2018
dftregistration_min_max_3d.m
downsample_data.m include windowing in normcorre_batch Jan 30, 2017
h5_2_bin.m
han.m
license.txt
loadtiff.m faster load tiff Feb 9, 2018
loadtiff_old.m
mat2cell_ov.m moving files Dec 2, 2016
motion_metrics.m fix bug when nd = 3 Jan 17, 2017
normcorre.m
normcorre_batch.m merge remote-tracking branch 'upstream/master' Jan 29, 2019
normcorre_batch_even.m
pipeline.png moved fig to main folder Feb 1, 2017
read_file.m use mat lab's Tiff class May 8, 2018
read_raw_file.m
refreshdisp.m added more compact printing of progress Aug 31, 2017
register_frame.m
remove_boundaries.m
saveash5.m function to save data as an h5 file Feb 5, 2018
saveastiff.m better tiff reader/writer Dec 16, 2016
savefast.m moving files Dec 2, 2016
shift_reconstruct.m
split_frame.m rigid reg batch_even Jun 4, 2018

README.md

Join the chat at https://gitter.im/epnev/ca_source_extraction

NoRMCorre: Non-Rigid Motion Correction

This package provides a Matlab implementation of the NoRMCorre algorithm [1], and can be used for online piecewise rigid motion correction of 2d (planar) or 3d (volumetric) calcium imaging data.

Citation

If you find this package useful please cite the companion paper [1]:

@article{pnevmatikakis2017normcorre,
  title={NoRMCorre: An online algorithm for piecewise rigid motion correction of calcium imaging data},
  author={Pnevmatikakis, Eftychios A and Giovannucci, Andrea},
  journal={Journal of neuroscience methods},
  volume={291},
  pages={83--94},
  year={2017},
  publisher={Elsevier}
}

Synopsis

The algorithm operates by splitting the field of view into a set of overlapping patches. For each patch and each frame a rigid translation is estimated by aligning the patch against a template using an efficient, FFT based, algorithm for subpixel registration [2]. The estimated set of translations is further upsampled to a finer resolution to create a smooth motion field that is applied to a set of smaller overlapping patches. Extra care is taken to avoid smearing caused by interpolating overlapping patches with drastically different motion vectors. The registered frame is used to update the template in an online fashion by calculating a running/mean of past registered frames. The pipeline is summarized in the figure below.

Alt text

Code details

See the function demo.m for an example of the code. The algorithm is implemented in the function normcorre.m. If you have access to the parallel computing toolbox, then the function normcorre_batch.m can offer speed gains by enabling within mini-batch parallel processing. The user gives a dataset (either as 3D or 4D tensor loaded in RAM or memory mapped, or a pointer to a .tiff stack or .hdf5 file), and a parameters struct options. Optionally, an initial template can also be given. The algorithm can also be used for motion correction of 1p micro-endoscopic data, by estimating the shifts on high pass spatially filtered version of the data. See the script demo_1p.m for an example.

The algorithm can also be ran using the MotionCorrection object. See demo_mc_class.m for an example on how to use the object for 2p and 1p data.

The options struct can be set either manually or by using the function NoRMCorreSetParms.m. Some parameters of the options struct are the following:

Parameter name Description
d1,d2,d3 dimensions of field of view
grid_size size of non-overlapping portion of each patch the grid in each direction (x-y-z)
overlap_pre size of overlapping region in each direction before upsampling
mot_uf upsampling factor for smoothing and refinement of motion field
overlap_post size of overlapping region in each direction after upsampling
max_shift maximum allowed shift for rigid translation
max_dev maximum deviation of each patch from estimated rigid translation
upd_template update the template online after registering some frames
bin_width length of bin over which the registered frames are averaged to update the template
init_batch number of frames to be taken for computing initial template
iter number of times to go over the dataset
output_type type of output registered file
phase_flag flag for using phase correlation
correct_bidir check for offset due to bidirectional scanning (default: true)

The performance of registration can be evaluated using the function motion_metrics.m. The function simply computes the correlation coefficient of each (registered) frame, with the mean (registered) frame across time, the mean registered frame, and its crispness.

Developers

Eftychios A. Pnevmatikakis, Flatiron Institure, Simons Foundation

External packages

This package includes functions from the following packages

Integrations

This package will be integrated with the Matlab code for source extraction and deconvolution using CNMF.

A python version of this algorithm developed from Andrea A. Giovannuci is included as part of the CaImAn package that provides a complete pipeline for calcium imaging data pre-processing.

Although the two implementations give almost identical results for the same input file, there are some slight differences in the way they are called and their capabilities. These differences are highlighted here.

More details, contact information, and citing NoRMCorre

Check the wiki for more details and some frequently asked questions.

Please use the gitter chat room for questions and comments, and create an issue for any bugs you might encounter.

If you find this package useful please cite the following paper:

Eftychios A. Pnevmatikakis and Andrea Giovannucci, NoRMCorre: An online algorithm for piecewise rigid motion correction of calcium imaging data, Journal of Neuroscience Methods, vol. 291, pp 83-94, 2017; doi: https://doi.org/10.1016/j.jneumeth.2017.07.031

Acknowledgements

Example dataset is kindly provided from Andrea Giovannucci, taken at Wang lab (Princeton University).

References

[1] Eftychios A. Pnevmatikakis and Andrea Giovannucci, NoRMCorre: An online algorithm for piecewise rigid motion correction of calcium imaging data, Journal of Neuroscience Methods, vol. 291, pp 83-94, 2017; doi: https://doi.org/10.1016/j.jneumeth.2017.07.031

[2] Guizar-Sicairos, M., Thurman, S. T., & Fienup, J. R. (2008). Efficient subpixel image registration algorithms. Optics letters, 33(2), 156-158. Matlab implementation available here.

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