Reference: https://doi.org/10.1016/j.cell.2019.05.050
Mu Y*, Bennett DV*, Rubinov M*, Narayan S, Yang CT, Tanimoto M, Mensh BD, Looger LL, Ahrens MB.
Glia accumulate evidence that actions are futile and suppress unsuccessful behavior.
Cell 2019 178:27-43.
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Apache Spark
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Advanced Normalization Tools (ANTs)
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h5py, matplotlib, nibabel, numpy>=1.13, pandas>=0.2, scipy, scikit-image, scikit-learn
- use pip to install:
pip install git+https://github.com/mikarubi/voluseg.git
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Download an example dataset folder:
https://www.dropbox.com/sh/psrj9lusohj7epu/AAAbj8Jbb3o__pyKTTDxPvIKa?dl=0
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Launch IPython with Spark.
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Import package and load default parameters.
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Set and save parameters (see
voluseg.parameter_dictionary??for details). -
Execute code sequentially to perform cell detection.
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The final output is in the file
cells0_clean.hdf5in the output directory.
# set up
import os
import pprint
import voluseg
# check for updates
voluseg.update()
# set and save parameters
parameters0 = voluseg.parameter_dictionary()
parameters0['dir_ants'] = '/path/to/ants/bin/'
parameters0['dir_input'] = '/path/to/input/images/'
parameters0['dir_output'] = '/path/to/output/directory/'
parameters0['registration'] = 'high'
parameters0['diam_cell'] = 5.0
parameters0['f_volume'] = 2.0
voluseg.step0_process_parameters(parameters0)
# load and print parameters
filename_parameters = os.path.join(parameters0['dir_output'], 'parameters.pickle')
parameters = voluseg.load_parameters(filename_parameters)
pprint.pprint(parameters)
print("process images.")
voluseg.step1_process_images(parameters)
print("align images.")
voluseg.step2_align_images(parameters)
print("mask images.")
voluseg.step3_mask_images(parameters)
print("detect cells.")
voluseg.step4_detect_cells(parameters)
print("clean cells.")
voluseg.step5_clean_cells(parameters)