Data
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 anonymisation [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 imperfect. 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.
The data set used with MIA lab is shared with students registered at Uni Bern. External users/students will have to access this data directly from the HCP webpage. Alternatively, most of this analysis works also on the Medical Segmentation Decathlon data sets - the hippocampus task is particularly easy and lightweight.
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.gzT2-weighted atlas image:
mni_icbm152_t2_tal_nlin_sym_09a.nii.gzBrain mask:
mni_icbm152_t1_tal_nlin_sym_09a_mask.nii.gz
Add these files to the ./data/atlas/ directory.
Random Forest Toy Example
To 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 is 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].http://research.microsoft.com/en-us/downloads/52d5b9c3-a638-42a1-94a5-d549e2251728/.
[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. Accessed from: http://www.sciencedirect.com/science/article/pii/S1053811913005351
[3] Milchenko, M. and Marcus, D., 2013. Obscuring surface anatomy in volumetric imaging data. Neuroinformatics, 11(1), pp.65-75. Accessed from: https://link.springer.com/article/10.1007/s12021-012-9160-3
[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. Accessed from: http://www.sciencedirect.com/science/article/pii/S089662730200569X
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