DISCo for the CIA: Deep learning, Instance Segmentation, and Correlations for Calcium Imaging Analysis
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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. Although many sophisticated methods have been proposed, the cell extraction from calcium imaging data can still be prohibitively laborious and require manual annotation and correction. 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.
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