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基于卷积神经网络的臂丛神经超声图像分割方法 Title:SegmentationofBrachialPlexusNerveUltrasoundImagesbasedonConvolutionalNeuralNetworks Abstract: Thesegmentationofbrachialplexusnervesfromultrasoundimageshassignificantclinicalimplicationsfordiagnosingandtreatingnerve-relateddisorders.However,manualidentificationofthesenervesistime-consumingandpronetohumanerrors.Therefore,developinganaccurateandefficientautomatedsegmentationmethodiscrucial.Inrecentyears,convolutionalneuralnetworks(CNNs)haveshownremarkableperformanceinimagesegmentationtasks.ThispaperproposesaCNN-basedapproachforthesegmentationofbrachialplexusnervesinultrasoundimages. 1.Introduction Thebrachialplexusisacomplexnetworkofnerveslocatedintheupperlimbthatinnervatesthemusclesandprovidessensoryinformation.Injuriesordiseasesaffectingthebrachialplexuscanresultinsignificantfunctionalimpairments.Therefore,accuratesegmentationofthesenervesishighlydesirablefordiagnosisandtreatmentplanning. 2.BrachialPlexusUltrasoundImaging Ultrasoundimagingisanon-invasive,cost-effective,andwidelyavailableimagingmodalityforvisualizingthebrachialplexus.However,duetothecomplexanatomicalstructureandvariabilityamongindividuals,segmentingbrachialplexusnervesfromultrasoundimagespresentsseveralchallenges,suchaslowcontrast,specklenoise,anddiscontinuousboundaries. 3.ConvolutionalNeuralNetworks Convolutionalneuralnetworks(CNNs)haverevolutionizedthefieldofcomputervisionanddemonstratedoutstandingperformanceinvarioustasks,includingimageclassification,objectdetection,andsemanticsegmentation.CNNsareparticularlywell-suitedforimagesegmentationtasksduetotheirabilitytocapturebothlocalandglobalfeatures. 4.ProposedMethodology Theproposedsegmentationmethodconsistsofseveralkeysteps:preprocessing,networkarchitecturedesign,training,andpost-processing. 4.1Preprocessing Preprocessingisperformedtoenhancetheclarityandqualityoftheultrasoundimages,includingdenoising,contrastenhancement,andnormalization. 4.2NetworkArchitectureDesign ACNNarchitectureisdesignedtoextractrelevantfeaturesandcla