Correspondence Transfer for the Registration of Multimodal Images

Gene expression data provide information on the location where certain genes are active; in order for this to be useful, such a location must be registered to an anatomical atlas. Because gene expression maps are considerably different from each other – t

CorrespondenceTransferfortheRegistrationofMultimodalImages

ZhaoYiStefanoSoatto

ComputerScienceDepartment,UniversityofCalifornia

LosAngeles,CA90095

{zyi,soatto}@cs.ucla.edu

Abstract

Geneexpressiondataprovideinformationonthelo-cationwherecertaingenesareactive;inorderforthistobeuseful,suchalocationmustberegisteredtoananatomicalatlas.Becausegeneexpressionmapsareconsiderablydifferentfromeachother–theydisplaytheexpressionofdifferentgenes–andfromtheanatomicalatlas,thisproblemiscurrentlyaddressedeitherman-uallybytrainedexperts,orbyneglectingallimagein-formationandonlyusingthepre-segmentedboundaries.Inthismanuscriptweconcentrateondatadiscrepancymeasuresthattakeintoaccountimageinformationwhenthisispresentinboththetargetandtemplateimages.Weexploitsuch“bi-lateral”structurestodrivethecor-respondenceprocessinregionswheretheintensityin-formationisinconsistent,analogouslytoa“motionin-painting”task.Althoughnogroundtruthcanbeestab-lished,andpriorinformationclearlyplaysakeyrole,weshowthatourmodelachievesdesirableresultsonsubjectivetestsvalidatedbyexpertsubjects.

1.Introduction

Establishingcorrespondencebetweendifferentim-agesiskeyforustoinferpropertiesoftheunderlyingscene.Thebasicassumptionisthatthereissomethingcommonbetweentheimages,modulodomaindeforma-tions(e.g.inducedbyviewpointchangesorbyscenede-formations)andrangedeformations(e.g.contrasttrans-formationsinducedbychangesinillumination,orbychangesofimagingmodality).Suchcommonalitymaybeabstract,ratherthanphysical,forinstancewhentheimagesportrayobjectsinthesamecategory,say“hip-pocampus,”eventhougheachimageportraysadifferentphysicalobject.Acrucialcomponentofanyapproachtoregistrationisthemechanismusedtocomparetwo(de-formed)images:Whilerange(intensity)similarityisa

naturalchoice,forinstancemeasuredinthesenseofL2[30]orTotalVariation[26],extremecontrastchangeshavebeensuccessfullytackledusingMutualInforma-tion[22].

Themostrecentdevelopmentsinmedicalimag-ing,however,arechallengingthesepremisesaltogether:Geneexpressiondataaregeneratedwithdifferentstains,highlightingdifferentgenes,withtheexpressgoalofmakingeachresultingimageasdifferentaspossiblefromtheothers,inordertomaximizetheirinformationcontent.Nevertheless,thepractitionerrequiresregister-ingsuchimagestoanatomicalatlases,inordertoascribetheactivityofagenetoaparticularanatomicalstruc-ture(Fig.1).Thesamegoesforregisteringfunctionalimaging(e.g.F-MRI)toanatomicalatlases,ataskthatisbyandlargeperformedmanuallybytrainedphysicians.Whilethisisdoableforahandfulofsubjects,system-aticstatisticalstudiesofgeneexpressiondatainlargepopulationscallforsomedegreeofautomation.Butwhatdoesitmeantoestablishcorrespondence,whenthereisnocommonunderlyingstructure,andwhenthedataaredesignedtobeasdifferent(“indepen-dent”)fromeachotheraspossible?Clearlyexpertpriorknowledgeofanatomyandbiologicalfunctionalityisin-dispensable,andseveralresearchgroupsareactivelyen-gagedinmodeling,learningandenforcingshapepriorsinsegmentationandregistration[16,25].Nevertheless,anyregistrationalgorithmmustalsotakeintoaccounttheavailabledata,andthisproblemhasbeenlargelyoverlookedintheliterature,wheremostlystandarddatatermsareused[15,24],orwhereonlytheboundaryin-formationistakenintoconsiderationandtherestofthedeformation eldisdeterminedbygenericregulariza-tion[8,20,27].Therefore,inthismanuscriptwefocusourattentionondevisingsuitabledatatermsforregister-ingmulti-modalimages.Ourgoalistodesignaschemetotakevisiblegeometricstructures(onecouldcallthem“landmarkregions”)intoaccountwhentheyarepresent

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