  Calcium imaging is one of the most important tools in neurophysiology as it
enables the observation of neuronal activity for hundreds of cells in parallel
and at single-cell resolution. In order to use the data gained with calcium
imaging, it is necessary to extract individual cells and their activity from
the recordings. We present DISCo, a novel approach for the cell segmentation in
calcium imaging videos. We use temporal information from the recordings in a
computationally efficient way by computing correlations between pixels and
combine it with shape-based information to identify active as well as
non-active cells. We first learn to predict whether two pixels belong to the
same cell; this information is summarized in an undirected, edge-weighted grid
graph which we then partition. In so doing, we approximately solve the NP-hard
correlation clustering problem with a recently proposed greedy algorithm.
Evaluating our method on the Neurofinder public benchmark shows that DISCo
outperforms all existing models trained on these datasets.
