HNCcorr algorithm for cell detection in calcium-imaging.
Project description
HNCcorr
2019/06/25 the HNCcorr software is currently being updated. The missing parts will be completey in the next few days.
The HNCcorr algorithm identifies cell bodies in two-photon calcium imaging movies. We provide a Python 3 (recommended) implementation as well as a Matlab implementation. The software is freely available for non-commercial use (see license for details).
The HNCcorr algorithm is described in detail in our eNeuro paper:
Spaen, Q., Asín-Achá, R., Chettih, S. N., Minderer, M., Harvey, C., and Hochbaum, D. S. (2019). HNCcorr: A novel combinatorial approach for cell identification in calcium-imaging movies. eNeuro, 6(2).
Example (Python)
from hnccorr import HNCcorr, Movie
from hnccorr.example import load_example_data
movie = Movie(
"Example movie", load_example_data() # downloads sample Neurofinder dataset
)
H = HNCcorr.from_config() # Initialize HNCcorr with default configuration
H.segment(movie)
H.segmentations # List of identified cells
H.segmentations_to_list() # Export list of cells (for Neurofinder)
Installation Instructions (Python 3)
You can install HNCcorr directly from the Python Package Index with pip:
pip install hnccorr
Or you can install your local copy by executing the following command from the main directory:
pip install .
On Windows you may need to install a C-compiler for Python.
Installation Instructions (Matlab)
The Matlab implementation was used to generate the results in the eNeuro manuscript and is now superseded by the Python implementation. The Matlab implementation is available in the matlab
folder. See the README file in this folder for instructions.
Documentation
The documentation is hosted at ReadTheDocs.
Hyperparameters
TO DO
Tests
The tests for HNCcorr use the pytest
package. You can execute them with the pytest
command in the main directory.
Project details
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