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Preprocessing pipeline on Brain MR Images through FSL and ANTs, including registration, skull-stripping, bias field correction, enhancement and segmentation.

License: MIT License

Python 100.00%

brain mri preprocessing fsl registration skull-stripping brain-segmentation ants enhancement bias-field-correction

brainprep's Introduction

Preprocessing on Brain MRI Sequence

This is a pipeline to do preprocessing on brain MR images of ADNI dataset
by using FMRIB Software Library (FSL) and Advanced Normalization Tools (ANTs).

1. Install FSL & ANTs

Download and install FSL as instructions here.
Compile ANTs from source code in Linux and macOS, or in Windows 10.

2. Install Python Packages

All required libraries are listed as below:

tqdm
numpy
scipy
nipype
nibabel
matplotlib
sciKit-fuzzy (optional)
scikit-learn (optional)

3. Download Dataset

The dataset used in this repo is AD and NC screening images of ADNI1 and ADNI2.
See README.md in data.

Here is one sample of original image.

4. Reorgnization Files

Switch the working directory to src. Run reorgnize.py, which merge ADNI1 and ADNI2 into one folder.

python reorgnize.py

5. Registration

Run registraion.py to transform images into the coordinate system of template by FSL FLIRT.

python registraion.py

The output of the above image from this step looks like:

6. Skull-Strpping

Run skull_stripping.py to remove skull from registrated images by FSL BET.

python skull_stripping.py

Output:

7. Bias Field Correction

Run bias_correction.py to remove bias-field signal from images by ANTs.

python bias_correction.py

Output:

8. Enhancement (optional)

Based on outputs from step 7, run enhancement.py to enhance images by histogram equalization.

python enahncement.py

9. Tissue Segmentation (optional)

Based on outputs from step 7, run segment.py to segment brain into GM, WM and CSF
by KMeans or Fuzzy-CMeans (you should change settings in script).

python segment.py

Or run fast_segment.py to do segmentation by FSL FAST.

python fast_segment.py

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brainprep's Issues

Can't Find the dataset used

I registered on the site you gave for the dataset but I can't find your dataset inside the website. Can you give any further indications, please?

scripts shouldn't have code that will run if imported

In general scripts shouldn't run functions when imported, which is what would happen with the scripts located in src/.

To only run code when being called as a script (and not from the import function) use:

if __name__ == '__main__':
    # run functions here

This will prevent code being run if the user wishes to import the functions defined within the modules.

No command "N4BiasFieldCorrection" found on host kim-X10DAi. Please check that the corresponding package is installed.

Thanks for your work，but an error occurred when I run bias_correction

No command "N4BiasFieldCorrection" found on host kim-X10DAi. Please check that the corresponding package is installed.

scns are in one file

GM,WM, and CSF all three are in one file. can you please tell how you separate all three and save in separate Dicom or nifti files

N4BiasFieldCorrection issues

I encountered the following problem when running bias_correction.py
OSError: No command "N4BiasFieldCorrection" found on host ubuntu. Please check that the corresponding package is installed.

But I have successfully installed ANTs, and when I try to run which antsRegistration* in the terminal, it returns the Ant installation directory /opt/ANTs/bin//antsRegistration

So how to solve the problem? Looking forward to your reply，thx!

Qustions about application in general brain preprocessing

Hi, I want to apply the pipeline to process nomral mri with 1 nii file for each subject. But I currently has no access to test dataset used in the profile, I am not clear about the reorganize and registration part, wether they work the same or just to skip the steps. A little bit explaination about relationship between NC1/2 and AD1/2 can help a lot, like whether they are from the same subject at diffrent stage and whether they are different modalities. Thanks!