Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration
Name
Yeo-2010-Spherical Demons Fast Diffeomorphic Landmark-Free Surface Registration.pdf
Size
1.29 MB
Format
Adobe PDF
Checksum (MD5)
47b9f7c1dc651ca45b7e18ac7d4d0ac0
Author(s)
Yeo, Boon Thye Thomas
Golland, Polina
Sabuncu, Mert R.
Fischl, Bruce
Vercauteren, Tom
Ayache, Nicholas
Date Issued
March 2010
Journal
IEEE transactions on medical imaging
Publisher
Institute of Electrical and Electronics Engineers
Citation
Yeo, B.T.T. et al. “Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration.” Medical Imaging, IEEE Transactions on 29.3 (2010): 650-668. © 2010, IEEE
Version
Final published version
Abstract
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.
Subjects
Cortical registration
Demons
Diffeomorphism
Spherical registration
Surface registration
Vector field interpolation
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/tmi.2009.2030797
