  Miniscope calcium imaging is increasingly being used to monitor large
populations of neuronal activities in freely behaving animals. However, due to
the high background and low signal-to-noise ratio of the single-photon based
imaging used in this technique, extraction of neural signals from the large
numbers of imaged cells automatically has remained challenging. Here we
describe a highly accurate framework for automatically identifying activated
neurons and extracting calcium signals from the miniscope imaging data, seeds
cleansing Constrained Nonnegative Matrix Factorization (sc-CNMF). This sc-CNMF
extends the conventional CNMF with two new modules: i) a neural enhancing
module to overcome miniscope-specific limitations, and ii) a seeds cleansing
module combining LSTM to rigorously select and cleanse the set of seeds for
detecting regions-of-interest. Our sc-CNMF yields highly stable and superior
performance in analyzing miniscope calcium imaging data compared to existing
methods.
