Data

Medical Images

The dataset consists of 3 tesla head MR images of 30 unrelated healthy subjects from the Human Connectome Project (HCP) dataset of healthy volunteers [2]. For each subject, the following data is available:

• T1-weighted (T1w) MR image volume, not skull-stripped (but defaced for anonymization [3]), with a bias field correction

• T2-weighted (T2w) MR image volume, processed the same way as the T1w image

• Both modalities in native T1w subject-space

• The ground truth label map and brain mask in native subject-space

• Affine transformation to align the images to the atlas (see below)

The ground truth labels are generated by FreeSurfer 5.3 (e.g., [4]) and are not manual expert annotations. As you will see when opening some example label maps, the automated labelling is not perfect. This is a common problem in the MIA domain, often real expert annotations are sparse and a “silver-standard” ground truth has to be used.

Atlas

The MR image and label files with mni prefix are registered to the MNI152 atlas using nonlinear FNIRT.

• T1-weighted atlas image: mni_icbm152_t1_tal_nlin_sym_09a.nii.gz

• T2-weighted atlas image: mni_icbm152_t2_tal_nlin_sym_09a.nii.gz

• Brain mask: mni_icbm152_t1_tal_nlin_sym_09a_mask.nii.gz

Add these files to the ./data/atlas/ directory.

Random Forest Toy Example

The get a feeling of what a random forest, the type of machine learning classifier used to classify voxels in the brain tissues at interest, does, toy example data are provided. The toy example data files in the data directory (exp1_n2.txt, …) are taken from the Sherwood library [1].

References

[1]

Microsoft Research, Sherwood C++ and C# code library for decision forests, 2012. [Online]. Available: http://research.microsoft.com/en-us/downloads/52d5b9c3-a638-42a1-94a5-d549e2251728/. [Accessed: 08-Sep-2020].

[2]

Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E., Yacoub, E., Ugurbil, K. and Wu-Minn HCP Consortium, 2013. The WU-Minn human connectome project: an overview. Neuroimage, 80, pp.62-79.

[3]

Milchenko, M. and Marcus, D., 2013. Obscuring surface anatomy in volumetric imaging data. Neuroinformatics, 11(1), pp.65-75.

[4]

Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., Van Der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S. and Montillo, A., 2002. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 33(3), pp.341-355.