NeuronID: An automatic toolkit for identifying neurons in two-photon calcium imaging data

Abstract

Two-photon calcium imaging has emerged as a powerful technique for monitoring neuronal activity in neuroscience; however, its data processing remains challenging. Here, we introduce NeuronID, an automatic toolkit designed to process two-photon calcium imaging data. The NeuronID toolkit features a modular architecture that includes motion correction, noise reduction, segmentation of neuronal components, and extraction of neuronal signals. Notably, the NeuronID toolkit offers an optimized strategy for segmenting neuronal components, which systematically integrates morphological boundary identification, cross-correlation analysis between pixels, and evaluation of neuronal signal quality. Compared to existing tools or manual annotation by experts, the NeuronID toolkit reduces the likelihood of over-segmentation while achieving near-human accuracy. Overall, this study provides an effective solution to the segmentation of neuronal components, offering a standardized analytical tool for processing two-photon calcium imaging data.