HOTARU: Automatic sorting system for large scale calcium imaging data

Currently, calcium imaging allows for the long-term recording of large-scale neuronal activity in diverse states. However, it remains difficult to extract neuronal dynamics from recorded imaging data. In this study, we propose an improved CNMF-based algorithm and an effective method for extracting cell shapes with fewer false positives and false negatives caused by image processing. We also showed that the values obtained during image processing can be combined and used for false positive determination of cells. For the CNMF algorithm, we combined cell-by-cell regularization and baseline shrinkage estimation, which greatly improved its stability and robustness. We applied these methods to artificial and real data and confirmed their effectiveness. Our method is simpler and faster, detects more cells with lower firing rates and signal-to-noise ratios, and enhance the quality of the extracted cell signals. These advances can improve the standard of downstream analysis and contribute to progress in neuroscience. ### Competing Interest Statement Y.H. received research fund from Takeda Pharmaceuticals, Fujitsu Laboratories, and Dwango.