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Proc Natl Acad Sci U S A. 2019 Apr 11. pii: 201812995. doi: 10.1073/pnas.1812995116. [Epub ahead of print]

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

Author information

Department of Biomedical Engineering, Duke University, Durham, NC 27708.
Department of Biomedical Engineering, Duke University, Durham, NC 27708;
Department of Neurobiology, Duke University, Durham, NC 27708.
Department of Ophthalmology, Duke University Medical Center, Durham, NC 27710.


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 layer with different neuron density. Finally, our work documents significant tabulation flaws in one of the most cited and active online scientific challenges in neuron segmentation. As our computationally fast method is an invaluable tool for a large spectrum of real-time optogenetic experiments, we have made our open-source software and carefully annotated dataset freely available online.


calcium imaging; deep learning; neuron segmentation; open source; two-photon microscopy


Conflict of interest statement

The authors declare no conflict of interest.

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