Imperial College London


Faculty of EngineeringDepartment of Bioengineering

Professor of Neurotechnology



+44 (0)20 7594 1533s.schultz Website




4.11Royal School of MinesSouth Kensington Campus






BibTex format

author = {Reynolds, SC and Abrahamsson, T and Schuck, R and Sjöström, PJ and Schultz, SR and Dragotti, PL},
doi = {10.1523/ENEURO.0012-17.2017},
journal = {Eneuro},
title = {ABLE: an Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data},
url = {},
volume = {4},
year = {2017}

RIS format (EndNote, RefMan)

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 achieves a 67.5% success rate.
AU - Reynolds,SC
AU - Abrahamsson,T
AU - Schuck,R
AU - Sjöström,PJ
AU - Schultz,SR
AU - Dragotti,PL
DO - 10.1523/ENEURO.0012-17.2017
PY - 2017///
SN - 2373-2822
TI - ABLE: an Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data
T2 - Eneuro
UR -
UR -
VL - 4
ER -