Evaluation of volume-based and surface-based brain image registration methods

Abstract

Establishing correspondences across brains for the purposes of comparison and group analysis is almost universally done by registering images to one another either directly or via a template. However, there are many registration algorithms to choose from. A recent evaluation of fully automated nonlinear deformation methods applied to brain image registration was restricted to volume-based methods. The present study is the first that directly compares some of the most accurate of these volume registration methods with surface registration methods, as well as the first study to compare registrations of whole-head and brain-only (de-skulled) images. We used permutation tests to compare the overlap or Hausdorff distance performance for more than 16,000 registrations between 80 manually labeled brain images. We compared every combination of volume-based and surface-based labels, registration, and evaluation. Our primary findings are the following: 1. de-skulling aids volume registration methods; 2. custom-made optimal average templates improve registration over direct pairwise registration; and 3. resampling volume labels on surfaces or converting surface labels to volumes introduces distortions that preclude a fair comparison between the highest ranking volume and surface registration methods using present resampling methods. From the results of this study, we recommend constructing a custom template from a limited sample drawn from the same or a similar representative population, using the same algorithm used for registering brains to the template.

Figure 1

Volume-labeled and surface-labeled brain image data. Upper panel (LPBA40 sample): For the volume-labeled brain image data, we used updated versions of the 40 brains used to construct the LONI Probabilistic Brain Atlases (Shattuck et al. 2008, 56 labeled regions). Left to right: T1-weighted MRI coronal slice, extracted brain, manual labels, gray matter mask, cortical gray matter label volume (V), and the left hemisphere labels resampled onto the unit sphere (S, with different colors). Notice that some of the surface is missing labels (gray). Lower panel (FS40 sample): For the surface-labeled brain image data, we used the 40 brains used to construct FreeSurfer’s default cortical parcellation atlas (Desikan et al. 2006, 35 labeled regions). Left to right: T1-weighted MRI coronal slice, extracted brain, FreeSurfer-generated surfaces of the left and right hemispheres seen from the front, the same surfaces with manual labels, the left hemisphere labels projected on the unit sphere, and the labels resampled in the brain volume (with different colors). (Note: images are not at the same scale, the colors in the upper and lower panels do not correspond, and neither do the colors in the rightmost images correspond with those to the left.)