A Unified Variational Volume Registration Method Based on Automatically Learned Brain Structures |
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| Authors: | Carl Lederman Anand Joshi Ivo Dinov John Darrell Van Horn Luminita Vese Arthur Toga |
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| Affiliation: | 1.Department of Mathematics,University of California Los Angeles,Los Angeles,USA;2.Institute for Data Science, School of Nursing,University of Michigan,Ann Arbor,USA;3.Statistics Online Computational Resource, Michigan Institute for Data Science, School of Nursing,University of Michigan,Ann Arbor,USA;4.Department of Statistics, Center for Computational Biology,University of California, Los Angeles (UCLA),Los Angeles,USA |
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| Abstract: | We introduce a new volumetric registration technique that effectively combines active surfaces with the finite element method. The method simultaneously aligns multi-label automatic structural segmentation results, which can be obtained by the application of existing segmentation software, to produce an anatomically accurate 3D registration. This registration is obtained by the minimization of a single energy functional. Just like registering raw images, obtaining a 3D registration this way still requires solving a fundamentally ill-posed problem. We explain through academic examples as well as an MRI dataset with manual anatomical labels, which are hidden from the registration method, how the quality of a registration method can be measured and the advantages our approach offers. |
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