000010476 001__ 10476
000010476 005__ 20210129210523.0
000010476 0247_ $$2pmid$$apmid:20529736
000010476 0247_ $$2DOI$$a10.1109/TMI.2010.2049497
000010476 0247_ $$2WOS$$aWOS:000281925700008
000010476 0247_ $$2altmetric$$aaltmetric:21804344
000010476 037__ $$aPreJuSER-10476
000010476 041__ $$aeng
000010476 082__ $$a610
000010476 084__ $$2WoS$$aComputer Science, Interdisciplinary Applications
000010476 084__ $$2WoS$$aEngineering, Biomedical
000010476 084__ $$2WoS$$aEngineering, Electrical & Electronic
000010476 084__ $$2WoS$$aImaging Science & Photographic Technology
000010476 084__ $$2WoS$$aRadiology, Nuclear Medicine & Medical Imaging
000010476 1001_ $$0P:(DE-HGF)0$$aYeo, B.T.T.$$b0
000010476 245__ $$aLearning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex
000010476 260__ $$aNew York, NY$$bInstitute of Electrical and Electronics Engineers,$$c2010
000010476 300__ $$a1424 - 1441
000010476 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article
000010476 3367_ $$2DataCite$$aOutput Types/Journal article
000010476 3367_ $$00$$2EndNote$$aJournal Article
000010476 3367_ $$2BibTeX$$aARTICLE
000010476 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000010476 3367_ $$2DRIVER$$aarticle
000010476 440_0 $$011381$$aIEEE Transactions on Medical Imaging$$v29$$x0278-0062$$y7
000010476 500__ $$aManuscript received October 24, 2009; revised April 21, 2010; accepted April 22, 2010. Date of publication June 07, 2010; date of current version June 30, 2010. This work was supported in part by the NAMIC (NIH NIBIB NAMIC U54-EB005149), in part by the NAC (NIH NCRR NAC P41-RR13218), in part by the mBIRN (NIH NCRR mBIRN U24-RR021382), in part by the NIH NINDS R01-NS051826 Grant, in part by the NSF CAREER 0642971 Grant, in part by the National Institute on Aging (AG02238), in part by the NCRR (P41-RR14075, R01 RR16594-01A1), in part by the NIBIB (R01 EB001550, R01EB006758), in part by the NINDS (R01 NS052585-01), and in part by the MIND Institute. Additional support was provided by The Autism & Dyslexia Project funded by the Ellison Medical Foundation. The work of B. T. Thomas Yeo was supported by the A*STAR, Singapore. Asterisk indicates corresponding author.
000010476 520__ $$aImage 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.
000010476 536__ $$0G:(DE-Juel1)FUEK409$$2G:(DE-HGF)$$aFunktion und Dysfunktion des Nervensystems (FUEK409)$$cFUEK409$$x0
000010476 536__ $$0G:(DE-HGF)POF2-89574$$a89574 - Theory, modelling and simulation (POF2-89574)$$cPOF2-89574$$fPOF II T$$x1
000010476 588__ $$aDataset connected to Web of Science, Pubmed
000010476 65320 $$2Author$$aCross validation error
000010476 65320 $$2Author$$afunctional magnetic resonance imaging (fMRI)
000010476 65320 $$2Author$$ahistology
000010476 65320 $$2Author$$aill-posed
000010476 65320 $$2Author$$aleave one out error
000010476 65320 $$2Author$$alocal maxima
000010476 65320 $$2Author$$alocal minima
000010476 65320 $$2Author$$amodel selection
000010476 65320 $$2Author$$aobjective function
000010476 65320 $$2Author$$aparameter tuning
000010476 65320 $$2Author$$aregistration parameters
000010476 65320 $$2Author$$aregularization
000010476 65320 $$2Author$$aspace of local optima
000010476 65320 $$2Author$$atradeoff
000010476 650_2 $$2MeSH$$aAlgorithms
000010476 650_2 $$2MeSH$$aBrain: physiology
000010476 650_2 $$2MeSH$$aBrain Mapping: methods
000010476 650_2 $$2MeSH$$aCerebral Cortex: physiology
000010476 650_2 $$2MeSH$$aHumans
000010476 650_2 $$2MeSH$$aImage Enhancement: methods
000010476 650_2 $$2MeSH$$aImage Interpretation, Computer-Assisted: methods
000010476 650_2 $$2MeSH$$aInformation Storage and Retrieval: methods
000010476 650_2 $$2MeSH$$aMagnetic Resonance Imaging: methods
000010476 650_2 $$2MeSH$$aPattern Recognition, Automated: methods
000010476 650_2 $$2MeSH$$aReproducibility of Results
000010476 650_2 $$2MeSH$$aSensitivity and Specificity
000010476 650_7 $$2WoSType$$aJ
000010476 7001_ $$0P:(DE-HGF)0$$aSabuncu, M.R.$$b1
000010476 7001_ $$0P:(DE-HGF)0$$aVercauteren, T.$$b2
000010476 7001_ $$0P:(DE-HGF)0$$aHolt, D.J.$$b3
000010476 7001_ $$0P:(DE-Juel1)131631$$aAmunts, K.$$b4$$uFZJ
000010476 7001_ $$0P:(DE-Juel1)131714$$aZilles, K.$$b5$$uFZJ
000010476 7001_ $$0P:(DE-HGF)0$$aGolland, P.$$b6
000010476 7001_ $$0P:(DE-HGF)0$$aFischl, B.$$b7
000010476 773__ $$0PERI:(DE-600)2068206-2$$a10.1109/TMI.2010.2049497$$gVol. 29, p. 1424 - 1441$$p1424 - 1441$$q29<1424 - 1441$$tIEEE transactions on medical imaging$$v29$$x0278-0062$$y2010
000010476 8567_ $$uhttp://dx.doi.org/10.1109/TMI.2010.2049497
000010476 909CO $$ooai:juser.fz-juelich.de:10476$$pVDB
000010476 915__ $$0StatID:(DE-HGF)0010$$aJCR/ISI refereed
000010476 9141_ $$y2010
000010476 9132_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x0
000010476 9131_ $$0G:(DE-HGF)POF2-89574$$1G:(DE-HGF)POF3-890$$2G:(DE-HGF)POF3-800$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bProgrammungebundene Forschung$$lohne Programm$$vTheory, modelling and simulation$$x1
000010476 9201_ $$0I:(DE-Juel1)INM-1-20090406$$gINM$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x1
000010476 9201_ $$0I:(DE-Juel1)INM-2-20090406$$gINM$$kINM-2$$lMolekulare Organisation des Gehirns$$x0
000010476 9201_ $$0I:(DE-82)080010_20140620$$gJARA$$kJARA-BRAIN$$lJülich-Aachen Research Alliance - Translational Brain Medicine$$x2
000010476 970__ $$aVDB:(DE-Juel1)120808
000010476 980__ $$aVDB
000010476 980__ $$aConvertedRecord
000010476 980__ $$ajournal
000010476 980__ $$aI:(DE-Juel1)INM-1-20090406
000010476 980__ $$aI:(DE-Juel1)INM-2-20090406
000010476 980__ $$aI:(DE-82)080010_20140620
000010476 980__ $$aUNRESTRICTED
000010476 981__ $$aI:(DE-Juel1)INM-2-20090406
000010476 981__ $$aI:(DE-Juel1)VDB1046