This repository contains a dockerfile and code to perform the SIENA tool from FSL package with the a proposed pipeline presented later.

Requirements to perform the code:

• FSL v5.0.1 [1]
• Python v3.6.4
• SimpleITK v2.0.1 [2]
• the repository of intensity normalization [3]
• ROBEX [4]

The dockerfile copy and configure the files siena_standardisation.py and run_intensities.py with:

• Z-score method
• Fuzzy c-means based white matter segmentation method
• Kernel density estimation based white matter segmentation method
• Gaussian mixture model based white matter segmentation method
• Piece wise linear histogram matching method [5]
• White stripe method [6]
• RAVEL method [7]

The methods are implemented for the FSL-SIENA v5.0.1 pipeline

To use the dockerfile is necessary to have docker.io installed v19.03.8 or later and perform the following instructions

1. clone the repository
2. Download ROBEX
3. Move the folder named ROBEX to the cloned repository
4. Inside the local repository execute docker build .
   this is going to take some time (hours)
5. To bind the docker container to the folder data, use
   docker run -it --mount type=bind,src=/path/to/repository/data,dst=/data container_id_or_tag

To use the pipeline, here is an example with the kernel density estimation method from the data folder:

./../src/siena_standardisation.py -b mri_image_1.nii.gz -f mri_image_2.nii.gz -s kde -o /data/

Instructions:

Usage:  ./src/siena_standardisation.py -b path to baseline MRI T1-w scan (nii or nii.gz file)
        -b path to baseline MRI T1-w scan (nii or nii.gz file)
        -f path to follow up MRI T1-w scan (nii or nii.gz file)
        -s intensity standardisation method to use
        Options:
            zscore: z-zscore method
            fcm: fuzzy c-means based white matter segmentation
            gmm: gaussian mixture model based white matter segmentation
            kde: kernel density estimation based white matter segmentation (recommended)
            hm: piecewise linear histogram matching
            ws: white stripe method 
            RAVEL: Removal of artificial voxel effect by linear regression
        -o output directory

[1] M. Jenkinson, C. F. Beckmann, T. E. Behrens, M. W. Woolrich, and S. M. Smith, “Fsl,” Neuroimage, vol. 62, no. 2, pp. 782–790, 2012.
[2] R. Beare, B. Lowekamp, and Z. Yaniv, “Image segmentation, registration and characterization in r with simpleitk,” Journal of statistical software, vol. 86, 2018.
[3] J. C. Reinhold, B. E. Dewey, A. Carass, and J. L. Prince, “Evaluating the impact of intensity normalization on MR image synthesis,” in Medical Imaging 2019: Image Processing, vol. 10949, p. 109493H, International Society for Optics and Photonics, 2019.
[4] J. E. Iglesias, C.-Y. Liu, P. M. Thompson, and Z. Tu, “Robust brain extraction across datasets and comparison with publicly available methods,” IEEE transactions on medical imaging, vol. 30, no. 9, pp. 1617–1634, 2011.
[5] L. G. Nyúl, J. K. Udupa, and X. Zhang, “New variants of a method of mri scale standardization,” IEEE transactions on medical imaging, vol. 19, no. 2, pp. 143–150, 2000.
[6] R. T. Shinohara, E. M. Sweeney, J. Goldsmith, N. Shiee, F. J. Mateen, P. A. Calabresi, S. Jarso, D. L. Pham, D. S. Reich, C. M. Crainiceanu, et al., “Statistical normalization techniques for magnetic resonance imaging,” NeuroImage: Clinical, vol. 6, pp. 9–19, 2014.
[7] J.-P. Fortin, E. M. Sweeney, J. Muschelli, C. M. Crainiceanu, R. T. Shinohara, A. D. N. Initiative, et al., “Removing inter-subject technical variability in magnetic resonance imaging studies,” NeuroImage, vol. 132, pp. 198–212, 2016.