MRI preprocessing / segmentation in < 30s.
Update: adding skull-stripping prior to affine registration to external template for better registration results.
turboprep script requires the following softwares to be installed:
- FreeSurfer >= v7.4
- ANTs
- intensity-normalization
A Docker container will be included in the future.
=====[ turboprep ]=====
Usage: /usr/bin/turboprep <image_path> <output_folder> <template_path> [OPTIONS]
Options:
-t, --threads <threads> Threads (default: number of cores)
-s, --shrink-factor Bias field correction shrink factor (default: 3), see N4BiasFieldCorrection
-m, --modality <modality> Modality {t2,other,md,t1,pd,flair} (default is t1)
-r, --registration-type <type> Registration type {t,r,a} (default is 'a' (affine), see antsRegistrationSyNQuick.sh)
--no-bfc Skip bias field correction step
--keep Keep intermediate files
usage: turboprep-safe.py [-h] --inputs INPUTS --outputs OUTPUTS --template TEMPLATE [-m MODALITY] [-t THREADS] [-s SHRINK_FACTOR]
[-r REGISTRATION_TYPE] [--no-bfc NO_BFC] [--keep]
options:
-h, --help show this help message and exit
--inputs INPUTS text file where each line is the path of an image to process
--outputs OUTPUTS text file where each line is the path to an output
--template TEMPLATE path of template image
-m MODALITY, --modality MODALITY
Modality {t2,other,md,t1,pd,flair} (default is t1)
-t THREADS, --threads THREADS
Threads (default: number of cores)
-s SHRINK_FACTOR, --shrink-factor SHRINK_FACTOR
Bias field correction shrink factor (default: 3), see N4BiasFieldCorrection
-r REGISTRATION_TYPE, --registration-type REGISTRATION_TYPE
Registration type {t,r,a} (default is 'a' (affine), see antsRegistrationSyNQuick.sh)
--no-bfc NO_BFC text file listing the inputs for which to skip bias field correction
--keep Keep intermediate filesExample of --inputs file:
/path/to/images/input-a.nii.gz
/path/to/images/input-b.nii.gz
/path/to/images/input-c.nii.gz
Example of --outputs file:
/path/to/outputs/input-a/
/path/to/outputs/input-b/
/path/to/outputs/input-c/
Example of --no-bfc file (skip bias-field correction for input-a and input-c):
/path/to/images/input-a.nii.gz
/path/to/images/input-c.nii.gz
| Step n. | Description | Algorithm | Package |
|---|---|---|---|
| 0 | Intensity inhomogeneity correction | N4 [1] | ANTs |
| 1 | Skull stripping | SynthStrip [7] | FreeSurfer |
| 2 | Affine registration to template | Symmetric Diffeomorphic Image Registration (just affine registration) [2] | ANTs |
| 3 | Segmentation of brain tissues | SynthSeg [3] | FreeSurfer |
| 4 | Brain mask extraction | Thresholding the segmentation | FreeSurfer |
| 5 | Intensity normalization | WhiteStripe [4] | intensity-normalization |
Two alternatives powered by deep learning are:
- EasyReg [6] - implemented in FreeSurfer as
mri_easyreg - SynthMorph [7] - implemented in FreeSurfer as
mri_synthmorph
Both algorithms are great, but the overhead of loading the model make their running time slower compared to ANTs when performing affine image registration.
[1] Tustison, Nicholas J., et al. "N4ITK: improved N3 bias correction." IEEE transactions on medical imaging 29.6 (2010): 1310-1320.
[2] Avants, Brian B., et al. "Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain." Medical image analysis 12.1 (2008): 26-41.
[3] Billot, Benjamin, et al. "SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining." Medical image analysis 86 (2023): 102789.
[4] Shinohara, Russell T., et al. "Statistical normalization techniques for magnetic resonance imaging." NeuroImage: Clinical 6 (2014): 9-19.
[5] Iglesias, Juan Eugenio. "A ready-to-use machine learning tool for symmetric multi-modality registration of brain MRI." Scientific Reports 13.1 (2023): 6657.
[6] Hoffmann, M., et al. "SynthMorph: Learning image registration without images." IEEE Trans. Med. Imaging (2021).
[7] Hoopes, Andrew, et al. "SynthStrip: Skull-stripping for any brain image." NeuroImage 260 (2022): 119474.