brainextractor

A re-implementation of FSL's Brain Extraction Tool in Python.

Follows the algorithm as described in:

Smith SM. Fast robust automated brain extraction. Hum Brain Mapp.
2002 Nov;17(3):143-55. doi: 10.1002/hbm.10062. PMID: 12391568; PMCID: PMC6871816.

This code was originally made for a course project.

https://user-images.githubusercontent.com/3641187/190677589-be019bc6-60e4-4e96-8c71-266285ab0755.mp4

Install

To install, use pip to install this repo:

# install from pypi
pip install brainextractor

# install repo with pip
pip install git+https://github.com/vanandrew/brainextractor@main

# install from local copy
pip install /path/to/local/repo

NOTE: It is recommended to use brainextractor on Python 3.7 and above.

Usage

To extract a brain mask from an image, you can call:

# basic usage
brainextractor [input_image] [output_image]

# example
brainextractor /path/to/test_image.nii.gz /path/to/some_output_image.nii.gz

You can adjust the fractional intensity with the -f flag:

# with custom set threshold
brainextractor [input_image] [output_image] -f [threshold]

# example
brainextractor /path/to/test_image.nii.gz /path/to/some_output_image.nii.gz -f 0.4

To view the deformation process (as in the video above), you can use the -w flag to write the surfaces to a file. Then use brainextractor_render to view them:

# writes surfaces to file
brainextractor [input_image] [output_image] -w [surfaces_file]

# load surfaces and render
brainextractor_render [surfaces_file]

# example
brainextractor /path/to/test_image.nii.gz /path/to/some_output_image.nii.gz -w /path/to/surface_file.surfaces

brainextractor_render /path/to/surface_file.surfaces

If you need an explanation of the options at any time, simply run the --help flag:

brainextractor --help

If you need to call Brainextractor directly from python:

# import the nibabel library so we can read in a nifti image
import nibabel as nib
# import the BrainExtractor class
from brainextractor import BrainExtractor

# read in the image file first
input_img = nib.load("/content/MNI.nii.gz")

# create a BrainExtractor object using the input_img as input
# we just use the default arguments here, but look at the
# BrainExtractor class in the code for the full argument list
bet = BrainExtractor(img=input_img)

# run the brain extraction
# this will by default run for 1000 iterations
# I recommend looking at the run method to see how it works
bet.run()

# save the computed mask out to file
bet.save_mask("/content/MNI_mask.nii.gz")