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README.md (1.74 kB)
.NII
subspace_scaled.nii (1.01 GB)
.NII
Template_0.nii (10.33 MB)
.NII
Template_1.nii (10.33 MB)
.NII
Template_2.nii (10.33 MB)
.NII
Template_3.nii (10.33 MB)
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Template_4.nii (10.33 MB)
.MAT
model_variables.mat (148 kB)
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8 files

SPM Shape-PCA model

This dataset contains the parameters of a random orbit model, where the template represents the mean shape of a population and deviations from this mean shape are encoded by geodesics sampled from a multivariate Gaussian distribution.

  • shape_pca_template_{01234}.nii contain the template with increasingly refined levels of details.
  • shape_pca_subspace_scaled.nii contains a low-dimensional orthogonal subspace from which geodesic-encoding velocities are sampled.

model_variables.mat contains variables

  • A (100x100 matrix) - contains the covariance of the learned prior over latent variables; i.e., their distribution in the population.
  • Az (100x100 matrix) - contains the posterior covariance over latent variables; i.e., the uncertainty about the true value of any latent code.
  • lam - contains the precision (i.e., inverse of variance) of the residual noise not captured by the 100-dimensional subspace.

References

  • Ashburner, J., Brudfors, M., Bronik, K. and Balbastre, Y., 2019. An algorithm for learning shape and appearance models without annotations. Medical image analysis, 55, pp.197-215.
  • Balbastre, Y., Brudfors, M., Bronik, K. and Ashburner, J., 2018. Diffeomorphic brain shape modelling using Gauss-Newton optimisation. MICCAI 2018, pp. 862-870.


Funding

Wellcome Centre for Human Neuroimaging

Wellcome Trust

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