Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 

volumetric segmentation pipeline

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.

Contact: Mika Rubinov, mika.rubinov at vanderbilt.edu

dependencies

  • Apache Spark

  • Advanced Normalization Tools (ANTs)

  • h5py, matplotlib, nibabel, numpy, pandas, scipy, scikit-image, scikit-learn

installation

  • use pip to install: pip install git+https://github.com/mikarubi/voluseg.git

example usage

  • Download an example dataset folder:

    https://www.dropbox.com/sh/psrj9lusohj7epu/AAAbj8Jbb3o__pyKTTDxPvIKa?dl=0

  • Launch IPython with Spark.

  • Import package and load default parameters.

  • Set and save parameters (see voluseg.parameter_dictionary?? for details).

  • Execute code sequentially to perform cell detection.

  • The final output is in the file cells0_clean.hdf5 in the output directory.

example code

# 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/volumes/'
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 volumes.")
voluseg.step1_process_volumes(parameters)

print("align volumes.")
voluseg.step2_align_volumes(parameters)

print("mask volumes.")
voluseg.step3_mask_volumes(parameters)

print("detect cells.")
voluseg.step4_detect_cells(parameters)

print("clean cells.")
voluseg.step5_clean_cells(parameters)

pipeline output

parameters.pickle

  • parameter dictionary.

  • parameters = voluseg.load_parameters('parameters.pickle')

  • required as input to individual pipeline steps.

mask_plots

  • directory of average volume plane images

  • brain mask superimposed on brain volume

  • can be used to assess goodness of brain masks

transforms directory

  • directory of affine transforms for individual volumes

  • can be used to assess movement of individual volumes

  • can be used to register volumes from a concurrent recording

volume0.hdf5

  • background: estimated background fluorescence

  • block_valids: indices of blocks used for segmentation

  • block_xyz0/1: min/max block xyz coordinates

  • n_blocks: total number of blocks

  • n_voxels_cells: approximate number of voxels in each cell

  • thr_intensity: brain-mask intensity threshold

  • thr_probability: brain-mask probability threshold

  • volume_mean/mask/peak: volume mean/mask/local peak intensity

mean_timeseries.hdf5

  • mean_baseline: baseline of detrended volume-mean timeseries

  • mean_timeseries: detrended volume-mean timeseries

  • mean_timeseries_raw: raw volume-mean timeseries

  • timepoints: indices of timepoints used for cell segmentation

cells0_clean.hdf5

  • background: estimated background fluorescence

  • cell_baseline: computed cell baselines

  • cell_timeseries: detrended [+ optionally filtered] cell timeseries

  • cell_timeseries_raw: raw cell timeseries (direct output of segmentation)

  • cell_weights: cell spatial footprints (spatial NMF components)

  • cell_x/y/z: cell coordinates

  • n/t: number of cells/timepoints

  • volume_id: cell ids represented on a volume

  • volume_weight: cell spatial footprints represent on a volume

  • x/y/z: volume dimensions

About

pipeline for volumetric cell segmentation

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages