Fast and robust active neuron segmentation in two-photon calcium imaging using spatiotemporal deep learning.

ABSTRACT: Calcium imaging records large-scale neuronal activity with cellular resolution in vivo. Automated, fast, and reliable active neuron segmentation is a critical step in the analysis workflow of utilizing neuronal signals in real-time behavioral studies for discovery of neuronal coding properties. Here, to exploit the full spatiotemporal information in two-photon calcium imaging movies, we propose a 3D convolutional neural network to identify and segment active neurons. By utilizing a variety of two-photon microscopy datasets, we show that our method outperforms state-of-the-art techniques and is on a par with manual segmentation. Furthermore, we demonstrate that the network trained on data recorded at a specific cortical layer can be used to accurately segment active neurons from another lay

SUBMITTER: Soltanian-Zadeh S 

PROVIDER: S-EPMC6486774 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature