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

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Soltanian-Zadeh, S; Sahingur, K; Blau, S; Gong, Y; Farsiu, S

Published Date

  • April 2019

Published In

Volume / Issue

  • 116 / 17

Start / End Page

  • 8554 - 8563

PubMed ID

  • 30975747

Pubmed Central ID

  • PMC6486774

Electronic International Standard Serial Number (EISSN)

  • 1091-6490

International Standard Serial Number (ISSN)

  • 0027-8424

Digital Object Identifier (DOI)

  • 10.1073/pnas.1812995116


  • eng