Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration

By B. T. T. Yeo, Mert Rory Sabuncu, Tom Kamiel Magda Vercauteren, Nicholas Ayache, Bruce Fischl, Polina Golland. IEEE Transactions on Medical Imaging, 2009

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We present the Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizors for the modified Demons objective function can be efficiently approximated on the sphere using iterative smoothing. Based on one parameter subgroups of diffeomorphisms, the resulting registration is diffeomorphic and fast. The Spherical Demons algorithm can also be modified to register a given spherical image to a probabilistic atlas. We demonstrate two variants of the algorithm corresponding to warping the atlas or warping the subject. Registration of a cortical surface mesh to an atlas mesh, both with more than 160 k nodes requires less than 5 min when warping the atlas and less than 3 min when warping the subject on a Xeon 3.2 GHz single processor machine. This is comparable to the fastest nondiffeomorphic landmark-free surface registration algorithms. Furthermore, the accuracy of our method compares favorably to the popular FreeSurfer registration algorithm. We validate the technique in two different applications that use registration to transfer segmentation labels onto a new image (1) parcellation of in vivo cortical surfaces and (2) Brodmann area localization in ex vivo cortical surfaces.

Subfields: Aerospace Engineering, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b567d43858fbc8f32e775b2ec368a7846da0a806

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Citations in the corpus

Building a Science of Individual Differences from fMRI. (2016, background): “Switching to cortical folding-based inter-subject alignment [20,21] has been shown to somewhat reduce functional mismatch [22,23] (but, see [24]), yet has not become common practice.”
Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. (2012, background): “Keywords: architectonic, areas, maps, registration, retinotopy”
Brain templates and atlases (2012, neutral): “…al., 2005; Christensen et al., 1997; Chung et al., 2001; Grenander and Miller, 2007; Guo et al., 2005; Lian and Davatzikos, 2011; Miller, 2004; Miller et al., 1993, 2002, 2006; Thompson and Toga, 1996, 1999; Thompson et al., 2000a,b; Wang et al., 2005; Yeo et al., 2010a; Yushkevich et al., 2008).”
Measuring structural-functional correspondence: Spatial variability of specialised brain regions after macro-anatomical alignment (2012, neutral): “Analysis (ICA) directly in cortex space (Formisano et al., 2004; Goebel et al., 2006) and to register brains to existing atlases (Yeo et al., 2010).”
FreeSurfer (2012, methodology): “…an array of surface-based tools for spherical filter banks including wavelets (Yeo et al., 2008), on fast diffeomorphic surface-based registration (Yeo et al., 2010a, 2010b), and for computing registration functionals that are optimized for specific tasks, such as alignment of Brodmann areas (Yeo…”
A Generative Model for Image Segmentation Based on Label Fusion (2010, neutral): “However, most of the ideas we discuss here can be easily extended to other modalities and applications, particularly with the recent development of fast algorithms for pairwise registration in other imaging domains [71], [73].”
Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI (2017, methodology): “Alignment between the architectonic and parcellation boundaries was numerically assessed using average Hausdorff distance (Yeo et al. 2010).”
Multimodal Surface Matching with Higher-Order Smoothness Constraints☆ (2017, methodology): “In terms of folding-based methods, both sMSMSTR and SD produced much lower and smoother distortions than FS; although it is important to note both were optimised for alignment of areal features (Robinson et al., 2014; Yeo et al., 2010), whereas FS was optimised for alignment of cortical folds.”
A Review on Medical Image Registration as an Optimization Problem (2017, background): “Its main application lies in tracking objects with small-scale movement in temporally different images [29, 30].”
MSM: a new flexible framework for Multimodal Surface Matching☆ (2014, neutral): “In such circumstances, pointwise registration algorithms such as FreeSurfer (Fischl et al., 1999b) and Spherical Demons (Yeo et al., 2010) are forced to obtain a match by expanding or contracting the additional anatomical folds in a way that is not biologically informed, which can lead to severe…”
Medical image registration: a review (2014, methodology): “The well-known demons algorithm and its variations (Thirion 1998; Guimond et al. 2001, 2002; Wang et al. 2005; Vercauteren et al. 2007; 2009; Yeo et al. 2010a; Gooya et al. 2011) are examples of optical ﬂow-based registration algorithms.”
Deformable Medical Image Registration: A Survey (2013, methodology): “The application of the Demons algorithm is not limited to scalar images and has been extended to di usion tensor images [57], multi-channel ones [58] as well as di erent geometries [59].”
Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality and Emotion (2017, background): “, Areas 44 and 45) after accounting for cortical folding patterns (Amunts et al., 1999; Amunts et al., 2000; Fischl et al., 2008; Yeo et al., 2010a).”