RT Journal Article
SR Electronic
T1 ABLE: An Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data
JF eneuro
JO eNeuro
FD Society for Neuroscience
SP ENEURO.0012-17.2017
DO 10.1523/ENEURO.0012-17.2017
VO 4
IS 5
A1 Stephanie Reynolds
A1 Therese Abrahamsson
A1 Renaud Schuck
A1 P. Jesper Sjöström
A1 Simon R. Schultz
A1 Pier Luigi Dragotti
YR 2017
UL http://www.eneuro.org/content/4/5/ENEURO.0012-17.2017.abstract
AB 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 “similar” 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’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.