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

## TL;DR

NeuronID is a new automatic toolkit that improves processing of two-photon calcium imaging data by accurately identifying neurons.

## Contribution

NeuronID introduces a modular toolkit with an optimized segmentation strategy for two-photon calcium imaging data.

## Key findings

- NeuronID reduces over-segmentation compared to existing tools or manual annotation.
- The toolkit achieves near-human accuracy in neuronal component segmentation.
- It integrates morphological boundary identification and cross-correlation analysis for improved results.

## 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.