TY - JOUR T1 - HNCcorr: A Novel Combinatorial Approach for Cell Identification in Calcium Imaging Movies JF - eneuro JO - eNeuro DO - 10.1523/ENEURO.0304-18.2019 SP - ENEURO.0304-18.2019 AU - Quico Spaen AU - Roberto Asín-Achá AU - Selmaan N. Chettih AU - Matthias Minderer AU - Christopher D. Harvey AU - Dorit S. Hochbaum Y1 - 2019/03/20 UR - http://www.eneuro.org/content/early/2019/03/20/ENEURO.0304-18.2019.abstract N2 - 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 employs a new method, named 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.Significance Calcium imaging is a method for recording neuronal activity at a cellular resolution that requires automated approaches to identify cells and their signals. HNCcorr is a novel algorithm that identifies these cells successfully and efficiently. HNCcorr is unique in that it addresses an optimization model and delivers a guaranteed globally optimal solution, thus ensuring a fully transparent link between the input data and the resulting cell identification. This contrasts with existing state-of-the-art approaches that produce only heuristic solutions to the underlying optimization model, and consequently may miss cells due to the sub-optimality of the generated solutions. ER -