Evaluation of volume-based and surface-based brain image registration methods
Introduction
Brain images are registered to one another (or to an average template) to establish correspondences of all kinds, such as across structures, patterns of functional activity, physiological data, and connectivity data. These correspondences enable comparison across time, task, and population. Brain image registrations are performed either on image volumes in their “native” space, or on surface representations of the brain.
Surface registration methods require computationally intensive extraction of a cortical surface, and may not be accurate for topologically different brains (such as lesioned and other pathological cases), but have been demonstrated to perform accurately under many conditions, including recent studies comparing surface features with cytoarchitectonic data (Rademacher et al., 1993, Hinds et al., 2008, Fischl et al., 2008). Significant advantages of performing registrations on a surface compared with in a volume include computational efficiency (less to register, one less degree of freedom), and distances along the cortical surface are more faithfully represented as geodesic distances along a surface rather than Euclidean distances across, for example, banks of a sulcus.
Most prior attempts to compare volume and surface registration methods have used function to gauge registration accuracy and compared a nonlinear surface-based spherical registration method with affine, Talairach-based linear or piecewise linear registration (Anticevic et al., 2008, Desai et al., 2005, Fischl et al., 1999, Thompson and Toga, 1996). Hinds et al. (2008) compared the quality of two atlases, one constructed using FreeSurfer spherical registration and the other using a single volume-based nonlinear registration method, according to the cumulative probability of a region (V1); this atlas comparison is a more indirect comparison than evaluating pairwise registrations.
In this study, we attempt to directly compare surface with volume registration methods. For the volume registration methods, we selected SyN (Avants et al., 2008) and ART (Ardekani et al., 1995, Ardekani et al., 2005), the only methods that attained top rank for all tests and for all four label sets and populations used in a recent, large-scale evaluation of brain image registration methods (Klein et al., 2009).
For the surface registration methods, we selected FreeSurfer (http://surfer.nmr.mgh.harvard.edu/) (Fischl et al., 1999) and Spherical Demons (Yeo et al., 2010). FreeSurfer is the most widely used of the fully automated, surface-based, brain image analysis software packages that perform registration without requiring landmarks. FreeSurfer was recently shown to outperform affine registration and SPM Normalize (Ashburner and Friston, 1999) when applied to a pediatric population (Ghosh, personal communication). Spherical Demons is a recently introduced extension of the Demons algorithm (Thirion, 1998) to the sphere, which is reported to have comparable performance to FreeSurfer but runs at least an order of magnitude faster (Yeo et al., 2010). Other popular freely available surface registration methods include Caret (http://brainmap.wustl.edu) (Van Essen et al., 2001) and BrainVisa (http://brainvisa.info) (Cointepas et al., 2001). They were not included in this study because Caret still requires manually assigned landmarks (personal communication with Van Essen and Dierker) and we are not aware of any means to apply a nonlinear transform to an arbitrary set of labels within BrainVisa.
We conducted more than 16,000 registrations between 40 brain images, either directly to one another or via templates, with the brains represented as either volumes or as surfaces. The registration transforms were then applied to manually labeled versions of these images (on volumes and on surfaces) to evaluate registration accuracy. We used permutation tests to compare registration performance to first select the top-ranking volume and surface registration methods, and then we compared these selections with one another. The initial set from which we made our selection was the following: SyN and ART on brain images with and without skulls, SyN, FreeSurfer, and Spherical Demons via custom templates, and FreeSurfer via its default atlas.
Section snippets
Materials and methods
In this section, we describe the brain image and label data, custom template construction, selection of image pairs to be registered to one another, and our evaluation measures and analysis method. We performed these latter steps on the LPBA40 data (see below) using an OSX system (Mac Pro 2-Quad-Core (8-processor) Intel Xeon, 3 GHz, 6 GB RAM) with a 10.5 operating system, and on the FS40 data (see below) using a computer cluster at the Martinos Center at the Massachusetts General Hospital
Results
Table 1 presents whether a volume or surface registration method obtained a higher ranking for each of the four tests, according to permutation tests. Higher ranks were obtained for the cases where there is no resampling prior to registration (see Table 1).
Discussion
After performing thousands of registrations between brain images (as surfaces and as volumes), we confirmed that removing non-brain matter aids brain volume registration, custom-made optimal average templates improve registration over direct pairwise registration, and resampling errors introduced by converting volume labels to surfaces or surface labels to volumes can be used to make a fair comparison between volume and surface registration methods using present resampling methods. Evaluation
Acknowledgments
We would like to thank the reviewers for their comments, David Shattuck for making available an early second release of the publicly available LPBA40 whole-head MR and label data, and Douglas Greve for advice on estimating smoothness of the FreeSurfer spherical templates. The first author is grateful to his colleagues in the Division of Molecular Imaging and Neuropathology, and, as always, to his two closest colleagues Deepanjana and Ellora. This work was partially funded by the National
References (25)
- et al.
Effects of spatial transformation on regional brain volume estimates
NeuroImage
(2008) - et al.
Comparing surface-based and volume-based analyses of functional neuroimaging data in patients with schizophrenia
Neuroimage
(2008) - et al.
Quantitative comparison of algorithms for inter-subject registration of 3D volumetric brain MRI scans
J. Neurosci. Methods
(2005) - et al.
Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain
Med. Image Anal.
(2008) - et al.
Iconic feature based nonrigid registration: the PASHA algorithm
Comput. Vis. Image Underst.
(2003) - et al.
BrainVISA: software platform for visualization and analysis of multi-modality brain data
NeuroImage
(2001) - et al.
Volumetric vs. surface-based alignment for localization of auditory cortex activation
NeuroImage
(2005) - et al.
An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest
NeuroImage
(2006) - et al.
Accurate prediction of V1 location from cortical folds in a surface coordinate system
NeuroImage
(2008) - et al.
Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration
NeuroImage
(2009)
Construction of a 3D probabilistic atlas of human cortical structures
NeuroImage
Image matching as a diffusion process: an analogy with Maxwell's demons
Med. Image Anal.
Cited by (200)
Strategic Infarct Locations for Poststroke Depressive Symptoms: A Lesion- and Disconnection-Symptom Mapping Study
2023, Biological Psychiatry: Cognitive Neuroscience and NeuroimagingConnectome-wide Functional Connectivity Abnormalities in Youth With Obsessive-Compulsive Symptoms
2022, Biological Psychiatry: Cognitive Neuroscience and Neuroimaging