Subcortex Parcellation

This example shows how to perform multi-contrast subcortical parcellation with the MASSP algorithm on MP2RAGEME data by performing the following steps:

1. Downloading an open MP2RAGE datasets using

   nighres.data.download_MP2RAGEME_sample() [1]

2. Downloading the open AHEAD template using

   nighres.data.download_AHEAD_template() [2]

3. Register the data to the AHEAD brain template

   nighres.registration.embedded_antsreg() [3]

4. Subcortex parcellation with MASSP nighres.parcellation.massp() [4]

Note: MASSP labels are listed inside the corresponding module and can be accessed with nighres.parcellation.massp_17structures_label()

Import and download

First we import nighres and the os module to set the output directory Make sure to run this file in a directory you have write access to, or change the out_dir variable below.

import nighres
import os
import nibabel as nb

in_dir = os.path.join(os.getcwd(), 'nighres_examples/data_sets')
out_dir = os.path.join(os.getcwd(), 'nighres_examples/massp_parcellation')

We also try to import Nilearn plotting functions. If Nilearn is not installed, plotting will be skipped.

skip_plots = False
try:
    from nilearn import plotting
except ImportError:
    skip_plots = True
    print('Nilearn could not be imported, plotting will be skipped')

Now we download an example MP2RAGEME dataset, including a quantitative R1 map, a quantitative R2* map, and a QSM, all skull-stripped.

dataset = nighres.data.download_MP2RAGEME_sample(data_dir=in_dir)

Now we download the AHEAD template for coregistration to the atlas space.

template = nighres.data.download_AHEAD_template()

Co-registration First we co-register the subject to the AHEAD template, and save the transformation mappings in the out_dir specified above, using a subject ID as the base file_name.

ants = nighres.registration.embedded_antsreg_multi(
                        source_images=[dataset['qr1'],dataset['qr2s'],dataset['qsm']],
                        target_images=[template['qr1'],template['qr2s'],template['qsm']],
                        run_rigid=True, run_affine=True, run_syn=True,
                        rigid_iterations=10000,
                        affine_iterations=2000,
                        coarse_iterations=180,
                        medium_iterations=60, fine_iterations=30,
                        cost_function='MutualInformation',
                        interpolation='NearestNeighbor',
                        regularization='High',
                        ignore_affine=True,
                        save_data=True, file_name="sample-subject",
                        output_dir=out_dir)

Tip

in Nighres functions that have several outputs return a dictionary storing the different outputs. You can find the keys in the docstring by typing nighres.brain.mp2rage_skullstripping? or list them with skullstripping_results.keys()

To check if the skull stripping worked well we plot the brain mask on top of the original image. You can also open the images stored in out_dir in your favourite interactive viewer and scroll through the volume.

Like Nilearn, we use Nibabel SpatialImage objects to pass data internally. Therefore, we can directly plot the outputs using Nilearn plotting functions .

if not skip_plots:
    plotting.plot_anat(ants['transformed_source'],cut_coords=[0.0,0.0,0.0],
                      annotate=False,  draw_cross=False)
    plotting.plot_anat(template['qr1'],cut_coords=[0.0,0.0,0.0],
                      annotate=False,  draw_cross=False)

MASSP Parcellation

Finally, we use the MASSP algorithm to parcellate the subcortex

massp = nighres.parcellation.massp(target_images=[dataset['qr1'],dataset['qr2s'],dataset['qsm']],
                                map_to_target=ants['inverse'],
                                max_iterations=120, max_difference=0.1,
                                save_data=True, file_name="sample-subject",
                                output_dir=out_dir, overwrite=False)

Now we look at the topology-constrained segmentation MGDM created

if not skip_plots:
    plotting.plot_roi(massp['max_label'], dataset['qr1'],
                      annotate=False, black_bg=False, draw_cross=False,
                      cmap='cubehelix')
    plotting.plot_img(massp['max_proba'],
                      vmin=0, vmax=1, cmap='gray',  colorbar=True,
                      annotate=False,  draw_cross=False)

If the example is not run in a jupyter notebook, render the plots:

if not skip_plots:
    plotting.show()

References

[1]	Caan et al. (2018) MP2RAGEME: T1, T2*, and QSM mapping in one sequence at 7 tesla. DOI: 10.1002/hbm.24490

[2]	Alkemade et al (under review). The Amsterdam Ultra-high field adult lifespan database (AHEAD): A freely available multimodal 7 Tesla submillimeter magnetic resonance imaging database.

[3]	Avants et al (2008). Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. DOI: 10.1016/j.media.2007.06.004

[4]	Bazin et al. (in prep) Multi-contrast Anatomical Subcortical Structures Parcellation