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Evaluation of Volume-Based and Surface-Based Brain Image Registration Methods

Institution:	1New York State Psychiatric Institute, Columbia University, NY, NY 10032, USA. arno@binarybottle.com 2Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA 3Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104–2644, USA 4Cognitive Neuroscience Lab, Harvard University, USA 5Athinoula A Martinos Center, Massachusetts General Hospital, USA 6Nathan Kline Institute, Orangeburg, NY 10962, USA
Publisher:	Elsevier Science
Publication Date:	May-2010
Journal:	Neuroimage
Volume Number:	51
Issue Number:	1
Pages:	214-20
Citation:	Neuroimage. 2010 May 15;51(1):214-20.
PubMed ID:	20123029
PMCID:	PMC2862732
Appears in Collections:	NA-MIC
Sponsors:	AG02238 (AG) funded by NIA NIH HHS P41 RR14075 (RR) funded by NCRR NIH HHS R01 EB006758 (EB) funded by NIBIB NIH HHS R01 MH084029 (MH) funded by NIMH NIH HHS R01 NS052585-01 (NS) funded by NINDS NIH HHS R03 EB008201 (EB) funded by NIBIB NIH HHS R03 EB008673 (EB) funded by NIBIB NIH HHS U24 RR021382 (RR) funded by NCRR NIH HHS U54 EB005149 (EB) funded by NIBIB NIH HHS
Generated Citation:	Klein A., Ghosh S.S., Avants B., Yeo B.T.T., Fischl B., Ardekani B., Gee J.C., Mann J.J., Parsey R.V. Evaluation of Volume-Based and Surface-Based Brain Image Registration Methods. Neuroimage. 2010 May 15;51(1):214-20. PMID: 20123029. PMCID: PMC2862732.
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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.

Klein-NeuroImage2010-fig1.jpg (204.541kB)