STNeuroNet is 3-dimensional convolutional neural network (CNN) for segmenting "active" neurons from calcium imaging data. The network was implemented through NiftyNet, a TensorFlow-based open-source CNN platform. You can adapt the existing network to your imaging data.

• Pre- and post-processing steps for segmenting active neurons
• A 3D CNN for batch-processing of calcium imaging data
• MATLAB GUI for manual marking of calcium imaging data

• Anaconda with Python 3.5
• MATLAB 2017b and MATLAB Runtime version 9.3
  • Neural Network Toolbox, Image Processing Toolbox, and the GUI Layout Toolbox
  • MATLAB Runtime can be acquired from here
• Tensorflow-gpu 1.4 (CUDA Toolkit 8.0 and cuDNN v7.0 required. Detailed instructions can be found here.)
• NiftyNet version 0.2.0.post1

The how-to guides are available on the Wiki.

NiftyNet source code on GitHub

Allen Brain Observatory dataset

Neurofinder Challenge website

If you use any part of this software in your work, please cite Soltanian-Zadeh et al. 2019:

• S. Soltanian-Zadeh, K. Sahingur, S. Blau, Y. Gong, and S. Farsiu, "Fast and robust active neuron segmentation in two-photon calcium imaging using spatio-temporal deep-learning," Proceedings of the National Academy of Sciences (PNAS), 116(17), pp. 8554-8563, April 2019. DOI: 10.1073/pnas.1812995116

If you use NiftyNet in your work, please cite Gibson and Li, et al. 2018:

• E. Gibson*, W. Li*, C. Sudre, L. Fidon, D. I. Shakir, G. Wang, Z. Eaton-Rosen, R. Gray, T. Doel, Y. Hu, T. Whyntie, P. Nachev, M. Modat, D. C. Barratt, S. Ourselin, M. J. Cardoso^ and T. Vercauteren^ (2018) NiftyNet: a deep-learning platform for medical imaging, Computer Methods and Programs in Biomedicine. DOI: 10.1016/j.cmpb.2018.01.025

• Li W., Wang G., Fidon L., Ourselin S., Cardoso M.J., Vercauteren T. (2017) On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task. In: Niethammer M. et al. (eds) Information Processing in Medical Imaging. IPMI 2017. Lecture Notes in Computer Science, vol 10265. Springer, Cham. DOI: 10.1007/978-3-319-59050-9_28

STNeuroNet is released under the GNU License, Version 2.0.

We thank David Feng and Jerome Lecoq from the Allen Institute for providing the ABO data, Saskia de Vries and David Feng from the Allen Institute for useful discussions, Hao Zhao for the initial implementation of the GUI, and Leon Kwark for the manual marking of the data.