ABLE: An Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data

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...

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Bibliographic Details
Published in:eNeuro 2017-09, Vol.4 (5), p.ENEURO.0012-17.2017
Main Authors: Reynolds, Stephanie, Abrahamsson, Therese, Schuck, Renaud, Sjöström, P Jesper, Schultz, Simon R, Dragotti, Pier Luigi
Format: Article
Language:English
Subjects:
Action Potentials
Algorithms
Animals
Automation, Laboratory
Brain - cytology
Brain - metabolism
Calcium - metabolism
Computer Simulation
Image Processing, Computer-Assisted - methods
Methods/New Tools
Mice, Inbred C57BL
Microscopy, Fluorescence - methods
Models, Neurological
Neurons - cytology
Neurons - metabolism
Patch-Clamp Techniques
Pattern Recognition, Automated - methods
Time Factors
Tissue Culture Techniques
Voltage-Sensitive Dye Imaging - methods
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Summary: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 dataset, containing synchronously spiking cells, and a manually labelled mouse dataset, on which ABLE (the proposed method) achieves a 67.5% success rate.
ISSN:2373-2822
2373-2822
DOI:10.1523/eneuro.0012-17.2017