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Learning task-optimal registration cost functions for localizing cytoarchitecture and function in the cerebral cortex.

Image registration is typically formulated as an optimization problem with multiple tunable, manually set parameters. We present a principled framework for learning thousands of parameters of registration cost functions, such as a spatially-varying tradeoff between the image dissimilarity and regularization terms. Our approach belongs to the classic machine learning framework of model selection by optimization of cross-validation error. This second layer of optimization of cross-validation error over and above registration selects parameters in the registration cost function that result in good registration as measured by the performance of the specific application in a training data set. Much research effort has been devoted to developing generic registration algorithms, which are then specialized to particular imaging modalities, particular imaging targets and particular postregistration analyses. Our framework allows for a systematic adaptation of generic registration cost functions to specific applications by learning the "free" parameters in the cost functions. Here, we consider the application of localizing underlying cytoarchitecture and functional regions in the cerebral cortex by alignment of cortical folding. Most previous work assumes that perfectly registering the macro-anatomy also perfectly aligns the underlying cortical function even though macro-anatomy does not completely predict brain function. In contrast, we learn 1) optimal weights on different cortical folds or 2) optimal cortical folding template in the generic weighted sum of squared differences dissimilarity measure for the localization task. We demonstrate state-of-the-art localization results in both histological and functional magnetic resonance imaging data sets.

Pubmed ID: 20529736

Authors

  • Yeo BT
  • Sabuncu MR
  • Vercauteren T
  • Holt DJ
  • Amunts K
  • Zilles K
  • Golland P
  • Fischl B

Journal

IEEE transactions on medical imaging

Publication Data

July 27, 2010

Associated Grants

  • Agency: NIA NIH HHS, Id: AG02238
  • Agency: NIBIB NIH HHS, Id: K25 EB013649
  • Agency: NCRR NIH HHS, Id: P41 RR013218
  • Agency: NCRR NIH HHS, Id: P41 RR014075
  • Agency: NCRR NIH HHS, Id: P41-RR13218
  • Agency: NCRR NIH HHS, Id: P41-RR14075
  • Agency: NIA NIH HHS, Id: R01 AG022381
  • Agency: NIBIB NIH HHS, Id: R01 EB001550
  • Agency: NIBIB NIH HHS, Id: R01 EB006758
  • Agency: NINDS NIH HHS, Id: R01 NS051826
  • Agency: NINDS NIH HHS, Id: R01 NS052585
  • Agency: NINDS NIH HHS, Id: R01 NS052585-01
  • Agency: NCRR NIH HHS, Id: R01 RR016594
  • Agency: NCRR NIH HHS, Id: R01 RR16594-01A1
  • Agency: NINDS NIH HHS, Id: R01-NS051826
  • Agency: NIBIB NIH HHS, Id: R01EB001550
  • Agency: NIBIB NIH HHS, Id: R01EB006758
  • Agency: NCRR NIH HHS, Id: U24 RR021382
  • Agency: NCRR NIH HHS, Id: U24-RR021382
  • Agency: NIBIB NIH HHS, Id: U54 EB005149
  • Agency: NIBIB NIH HHS, Id: U54-EB005149

Mesh Terms

  • Algorithms
  • Brain
  • Brain Mapping
  • Cerebral Cortex
  • Humans
  • Image Enhancement
  • Image Interpretation, Computer-Assisted
  • Information Storage and Retrieval
  • Magnetic Resonance Imaging
  • Pattern Recognition, Automated
  • Reproducibility of Results
  • Sensitivity and Specificity