Before revision:  We present DISCo, a novel approach for the cell segmentation in Calcium Imaging Analysis (CIA) that combines the advantages of Deep learning with a state-of-the-art Instance Segmentation algorithm and uses temporal information from the recordings in a computationally efficient way by computing Correlations between pixels .
After revision:  We present DISCo, a novel approach for the cell segmentation in Calcium Imaging Analysis (CIA) that combines the advantages of Deep learning with a state-of-the-art Instance Segmentation algorithm and uses 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 .