# 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This code was originally made for a [course project](https://www.cse.wustl.edu/~taoju/cse554/).
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:
```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")
```
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