Braift MRI Segmefttatioft with Patch-based CNN A

Proceedingsofthe35thChineseControlConferenceJuly27-29,2016,Chengdu,ChinaBraiftMRISegmefttatioftwithPatch-basedCNNApproachAbstract:ZhipengCUI,JieYANG,YuQIAO*InstituteofImageProcessingandPatternRecognition,ShanghaiJiaoTongUniversityTheKeyLaboratoryofMinistryofEducationforSystemControlandInformationProcessing,ChinaBrainMagneticResonanceImage(MRI)playsanon-substitutiveroleinclinicaldiagnosis.Thesymptomofmanydiseasescorrespondstothestructuralvariantsofbrain.AutomaticstructuresegmentationinbrainMRIisofgreatimportanceinmodernmedicalresearch.SomemethodsweredevelopedforautomaticsegmentingofbrainMRIbutfailedtoachievedesiredaccuracy.Inthispaper,weproposedanewpatch-basedapproachforautomaticsegmentationofbrainMRIusingconvolutionalneuralnetwork(CNN).EachbrainMRIacquiredfromasmallportionofpublicdatasetisfirstlydividedintopatches.AllofthesepatchesarethenusedfortrainingCNN,whichisusedforautomaticsegmentationofbrainMRI.Experimentalresultsshowedthatourapproachachievedbettersegmentationaccuracycomparedwithotherdeeplearningmethods.KeyWords:CNN,DeepLearning,BrainMRISegmentation,Patch-based1IntroductionThesegmentationofbrainMRIhasbeenahotareaofcomputervisionforseveralyears.SegmentationservesasanimportantstepforquantitativeanalysisinbrainMRIsandfortheresearchofbraindisorders.Indeed,structuralvariationinthebrainmaycausesomebraindisorders.Quantificationofstructuralvariationbymeasuringvolumesofregionofin­terest,canbeusedtoevaluateseverityofsomediseaseorevolutioninbrain[1].OnlywhenthelabelingisprocessedonMRI,thesemeasurementscanbeperformed.Segmenta­tionofMRIplaysanincreasinglyimportantroleinmedicalimageprocessingandanalysisasdigitalmedicalimagede­veloping.Thousandsofsegmentationmethodsweredevel­oped,whicharemainlyedge-basedandcontour-based[2].However,withthesemethods,itisaveryhardtaskforseg­mentingcomplexstructureofmedicalimagewithhighac­curacyrate[3].Deeplearningisatypeofmachinelearningapproaches,whicharisefromartificialneuralnetwork.DavidRumelhart,GeoffreyHintonandotherindividualsappliedbackpropaga­tionalgorithmtoartificialneuralnetwork,whichstartedma­chinelearningbasedonstatisticmodel[4].Artificialneuralnetworkwaslimitedtocomplexstructureandheavytrainingtime.Neuralnetworksreappearedasdeeplearningwhichcouldlearnfeaturehierarchywiththedevelopmentofhard­warein2006.Thepurposeofdeeplearningistolearnmul­tiplelevelsofrepresentationandfindinterestingstructureindata[5].Moderndeeplearningmethodscanrepresentfunc­tionsofincreasingcomplexityasthelayerisadded.Thewayofprocessingdataindeeplearningissimilartohumanbrain.Deeplearninghasmadegreatprogressintheseyears.IthasbeenappliedtoobjectrecognitiontasksinImageNetandfeaturelearningfromunlabeleddata[6].Multiplelevelsofrepresentationandunderlyingdistributionofthedatacanbeautomaticallylearnedwithdeeplearning[7].ConvolutionalNeuralNetworks(CNNs)areatypeoffullytrainablemodelswithmulti-layer[8].CNNsarebiologically-inspiredvariantsofMLPsderivedfromHubel­Wieselmodel,whicharesuccessfulinvisualprocessingal­gorithms.TheCNNsareavarietyofdeeplearningmethods,*Correspondingauthor:YuQIAO,[e-mail]urehierarchyfromimages[9].CNNhasadvantagesonprocessingimageswhosetrainingdataisnotlimitedtoID.TrainingdataofCNNcanbeIDacousticdata,2Dimagedataor3Dvideodata.ThehiddenlayersofaCNNconsistofconvolutionallayersandpoolinglayers[10].Featuremapsofthelayerrepresentthenum­beroffeaturesextractedfromprecedinglayer.Filtersinthelayerareusedtoprocessfeaturemapspassedfromformerlayer.Filtersareidenticaltothenumberoffeaturemaps.TherearequiteafewlimitationsinbrainMRIsegmen­tation.SegmentingbrainMRIwithtraditionalmethodsistime-consumingandrequirespriormedicalknowledge.Inaddition,trainingdataisanothermajorconcerninbrainMRI.ItisusuallyhardtocollectbrainMRI.ToovercomethedifficultiesinbrainMRIsegmentation,weimplementedapatch-basedCNNwithtimecostsufficientlylow.Thepro­posedCNNoutperformsotherCNNswithdifferentstruc­turesandANNsinsegmentationaccuracy.Patch-basedCNNhaswellsolvedaninsufficientamountoftrainingdata.2MethodThispaperusedthedatafromapublicdataset,whichcanbedownloadatCANDIneuroimagingaccesspointtocon­ducttheexperiment.JeanA.Frazier,et.al.manuallyseg­mentedMRIsinthisdataset[11].Itcomprises103MRIsfromfourdiagnosticgroups:BipolarDisorderwithPsy­chosis,BipolarDisorderwithoutPsychosis,SchizophrenicSpectrumandhealthycontrol[12].Thesubjectsarefromthe6to17agegroup,includingchildrenandadolescents,femaleandmale.AlloftheimageswererecruitedattheMcLeanHospitalBrainImagingCenterona1.5-Teslamagneticres­onancescanner(GeneralElectricSignaScanner)[13].2.1DataPreprocessingInthispaper,weextractedafewsmallsetsofMRIsfromthedatasetrandomly,eachsetconsistsof4to5MRIs.WedividedMRIswhosesizeis256x256to32x32and13x13patchesaccordingtothelabeloneachpixel.QuietafewpatchesextractedfrombrainMRIareuselessduetoimag­ingmodality.About25000of65536patchesareleft:asourtrainingset.Eachpixelismarkedbythelabelofcentralpixelineach32x32or13x13patch.Trainingsetcontainsabout100000patchesusedtotrainnetworks.7026Informula(2),(3jdenotescoefficientofpooling.Re