The third class of techniques uses a matrix factorization model to decompose a movie into the spatial and temporal properties of the individual neuronal signals. Shape-based techniques are typically applied by compressing the movie into a summary image obtained by averaging over the time dimension. Shape-based identification methods locate the characteristic shapes of cells using deep learning or dictionary learning ( Pachitariu et al., 2013).
This process has been reported to be highly labor intensive ( Resendez et al., 2016) and may miss cells with a low signal-to-noise ratio or a low activation frequency. Semi-manual ROI detection techniques rely on the user’s input for detecting and segmenting cells. There are three classes of existing techniques for cell identification in calcium-imaging movies: semi-manual region of interest (ROI) detection ( Kaifosh et al., 2014 Driscoll et al., 2017), shape-based detection algorithms, and matrix factorization algorithms ( Mukamel et al., 2009 Pnevmatikakis and Paninski, 2013 Pnevmatikakis et al., 2013a, 2016 Diego-Andilla and Hamprecht, 2014 Maruyama et al., 2014 Pachitariu et al., 2016 Levin-Schwartz et al., 2017). Consequently, there is a great need for automated approaches for the extraction of neuronal activity from imaging movies.
Pixel 3 f1 2019 image manual#
However, the manual postprocessing needed to extract the activity of single neurons requires tens of hours per dataset. Using genetically encoded calcium indicators and fast laser-scanning microscopes, it is now possible to record thousands of neurons simultaneously. We believe HNCcorr is an important addition to the toolbox for analysis of calcium-imaging movies.Ĭalcium imaging has become a standard method to measure neuronal activity in vivo ( Stosiek et al., 2003). The effectiveness of HNCcorr is demonstrated by its top performance on the Neurofinder cell identification benchmark. HNCcorr also uses a new method, called “similarity squared”, for measuring similarity between pixels in calcium-imaging movies.
HNCcorr guarantees a globally optimal solution to the underlying optimization problem as well as minimal dependence on initialization techniques. HNC identifies cells as coherent clusters of pixels that are highly distinct from the remaining pixels. HNCcorr relies on the combinatorial clustering problem HNC (Hochbaum’s Normalized Cut), which is similar to the Normalized Cut problem of Shi and Malik, a well known problem in image segmentation. We introduce the HNCcorr algorithm for cell identification in calcium-imaging datasets that addresses these shortcomings. Still, existing algorithms to detect and extract activity signals from calcium-imaging movies have major shortcomings. Calcium imaging is a key method in neuroscience for investigating patterns of neuronal activity in vivo.
0 kommentar(er)
