RT Journal Article
SR Electronic
T1 HNCcorr: A Novel Combinatorial Approach for Cell Identification in Calcium-Imaging Movies
JF eneuro
JO eNeuro
FD Society for Neuroscience
SP ENEURO.0304-18.2019
DO 10.1523/ENEURO.0304-18.2019
VO 6
IS 2
A1 Quico Spaen
A1 Roberto Asín-Achá
A1 Selmaan N. Chettih
A1 Matthias Minderer
A1 Christopher Harvey
A1 Dorit S. Hochbaum
YR 2019
UL http://www.eneuro.org/content/6/2/ENEURO.0304-18.2019.abstract
AB 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.