@article {ReynoldsENEURO.0012-17.2017, author = {Reynolds, Stephanie and Abrahamsson, Therese and Schuck, Renaud and Sj{\"o}str{\"o}m, P. Jesper and Schultz, Simon R. and Dragotti, Pier Luigi}, title = {ABLE: An Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data}, volume = {4}, number = {5}, year = {2017}, doi = {10.1523/ENEURO.0012-17.2017}, publisher = {Society for Neuroscience}, abstract = {We present an algorithm for detecting the location of cells from two-photon calcium imaging data. In our framework, multiple coupled active contours evolve, guided by a model-based cost function, to identify cell boundaries. An active contour seeks to partition a local region into two subregions, a cell interior and exterior, in which all pixels have maximally {\textquotedblleft}similar{\textquotedblright} time courses. This simple, local model allows contours to be evolved predominantly independently. When contours are sufficiently close, their evolution is coupled, in a manner that permits overlap. We illustrate the ability of the proposed method to demix overlapping cells on real data. The proposed framework is flexible, incorporating no prior information regarding a cell{\textquoteright}s morphology or stereotypical temporal activity, which enables the detection of cells with diverse properties. We demonstrate algorithm performance on a challenging mouse in vitro dataset, containing synchronously spiking cells, and a manually labelled mouse in vivo dataset, on which ABLE (the proposed method) achieves a 67.5\% success rate.}, URL = {http://www.eneuro.org/content/4/5/ENEURO.0012-17.2017}, eprint = {http://www.eneuro.org/content/4/5/ENEURO.0012-17.2017.full.pdf}, journal = {eNeuro} }