Globally Optimal Cortical Surface Matching With Exact Landmark Correspondence
In: LNCS ; Information Processing in Medical Imaging ; https://hal.science/hal-00974838 ; Information Processing in Medical Imaging, Jun 2013, Asilomar, California, United States. pp.487-498, 2013
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International audience ; We present a method for establishing correspondences be- tween human cortical surfaces that exactly matches the positions of given point landmarks, while attaining the global minimum of an ob- jective function that quantifies how far the mapping deviates from con- formality. On each surface, a conformal transformation is applied to the Euclidean distance metric, resulting in a hyperbolic metric with isolated cone point singularities at the landmarks. Equivalently, each surface is mapped to a hyperbolic orbifold: a pillow-like surface with each point landmark corresponding to a pillow corner. An initial surface-to-surface mapping exactly aligns the landmarks, and gradient descent is used to find the single, global minimum of the Dirichlet energy of the remainder of the mapping. Using a population of real MRI-based cortical surfaces with manually labeled sulcus endpoints as landmarks, we evaluate the approach by how much it distorts surfaces and by its biological plau- sibility: how well it aligns previously-unseen anatomical landmarks and by how well it promotes expected associations between cortical thickness and age. We show that, compared to a painstakingly-tuned approach that balances a tradeoff between minimizing landmark mismatch and Dirichlet energy, our method has similar biological plausibility, superior surface distortion, a better theoretical foundation, and fewer arbitrary parameters to tune. We also compare to conformal mapper in the spher- ical domain to show that sacrificing exact conformality of the mapping does not cause noticeable reductions in biological plausibility.
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Globally Optimal Cortical Surface Matching With Exact Landmark Correspondence
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Autor/in / Beteiligte Person: | Tsui, Alex ; Fenton, Devin ; Vuong, Phong ; Hass, Joel ; Koehl, Patrice ; Amenta, Nina ; Coeurjolly, David ; Decarli, Charles ; Carmichael, Owen ; Institute of Data Analysis and Visualization (IDAV) ; University of California Davis (UC Davis) ; University of California (UC)-University of California (UC) ; Department of Mathematics Univ California Davis (MATH - UC Davis) ; Geometry Processing and Constrained Optimization (M2DisCo) ; Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS) ; Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL) ; Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL) ; Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon) ; Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL) ; Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS) ; Neurology Department, University of California, Davis (UCDavis-Neuro) |
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Zeitschrift: | LNCS ; Information Processing in Medical Imaging ; https://hal.science/hal-00974838 ; Information Processing in Medical Imaging, Jun 2013, Asilomar, California, United States. pp.487-498, 2013 |
Veröffentlichung: | HAL CCSD ; Springer-Verlag, 2013 |
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