Abstract
Calcium imaging is a key method in neuroscience for investigating patterns of neuronal activity in vivo. Still, existing algorithms to detect and extract activity signals from calcium-imaging movies have major shortcomings. We introduce the HNCcorr algorithm for cell identification in calcium-imaging datasets that addresses these shortcomings. 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. HNC identifies cells as coherent clusters of pixels that are highly distinct from the remaining pixels. HNCcorr guarantees a globally optimal solution to the underlying optimization problem as well as minimal dependence on initialization techniques. HNCcorr also uses a new method, called “similarity squared”, for measuring similarity between pixels in calcium-imaging movies. The effectiveness of HNCcorr is demonstrated by its top performance on the Neurofinder cell identification benchmark. We believe HNCcorr is an important addition to the toolbox for analysis of calcium-imaging movies.
Footnotes
The authors declare no competing financial interests.
This research was supported by the Department of Health and Human Services (HHS) | National Institutes of Health (NIH) | National Institute of Mental Health: R01MH107620; HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS): R01NS089521; National Science Foundation (NSF) | Directorate for Engineering | Division of Civil, Mechanical and Manufacturing Innovation (CMMI): CMMI 1760102; a Boehringer Ingelheim Fund fellowship; a Herchel Smith graduate fellowship; a NSF graduate fellowship; the New York Stem Cell Foundation; and UC Berkeley | UC Berkeley Library | Berkeley Research Impact Initiative.
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