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@ARTICLE{Yeo:10476,
      author       = {Yeo, B.T.T. and Sabuncu, M.R. and Vercauteren, T. and Holt,
                      D.J. and Amunts, K. and Zilles, K. and Golland, P. and
                      Fischl, B.},
      title        = {{L}earning {T}ask-{O}ptimal {R}egistration {C}ost
                      {F}unctions for {L}ocalizing {C}ytoarchitecture and
                      {F}unction in the {C}erebral {C}ortex},
      journal      = {IEEE transactions on medical imaging},
      volume       = {29},
      issn         = {0278-0062},
      address      = {New York, NY},
      publisher    = {Institute of Electrical and Electronics Engineers,},
      reportid     = {PreJuSER-10476},
      pages        = {1424 - 1441},
      year         = {2010},
      note         = {Manuscript received October 24, 2009; revised April 21,
                      2010; accepted April 22, 2010. Date of publication June 07,
                      2010; date of current version June 30, 2010. This work was
                      supported in part by the NAMIC (NIH NIBIB NAMIC
                      U54-EB005149), in part by the NAC (NIH NCRR NAC
                      P41-RR13218), in part by the mBIRN (NIH NCRR mBIRN
                      U24-RR021382), in part by the NIH NINDS R01-NS051826 Grant,
                      in part by the NSF CAREER 0642971 Grant, in part by the
                      National Institute on Aging (AG02238), in part by the NCRR
                      (P41-RR14075, R01 RR16594-01A1), in part by the NIBIB (R01
                      EB001550, R01EB006758), in part by the NINDS (R01
                      NS052585-01), and in part by the MIND Institute. Additional
                      support was provided by The Autism $\&$ Dyslexia Project
                      funded by the Ellison Medical Foundation. The work of B. T.
                      Thomas Yeo was supported by the A*STAR, Singapore. Asterisk
                      indicates corresponding author.},
      abstract     = {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.},
      keywords     = {Algorithms / Brain: physiology / Brain Mapping: methods /
                      Cerebral Cortex: physiology / Humans / Image Enhancement:
                      methods / Image Interpretation, Computer-Assisted: methods /
                      Information Storage and Retrieval: methods / Magnetic
                      Resonance Imaging: methods / Pattern Recognition, Automated:
                      methods / Reproducibility of Results / Sensitivity and
                      Specificity / J (WoSType)},
      cin          = {INM-1 / INM-2 / JARA-BRAIN},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-1-20090406 / I:(DE-Juel1)INM-2-20090406 /
                      $I:(DE-82)080010_20140620$},
      pnm          = {Funktion und Dysfunktion des Nervensystems (FUEK409) /
                      89574 - Theory, modelling and simulation (POF2-89574)},
      pid          = {G:(DE-Juel1)FUEK409 / G:(DE-HGF)POF2-89574},
      shelfmark    = {Computer Science, Interdisciplinary Applications /
                      Engineering, Biomedical / Engineering, Electrical $\&$
                      Electronic / Imaging Science $\&$ Photographic Technology /
                      Radiology, Nuclear Medicine $\&$ Medical Imaging},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:20529736},
      UT           = {WOS:000281925700008},
      doi          = {10.1109/TMI.2010.2049497},
      url          = {https://juser.fz-juelich.de/record/10476},
}