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1、Multi-stageMulti-recursive-inputFullyConvolutionalNetworksforNeuronalBoundaryDetectionWeiShen1,2,BinWang1,YuanJiang1∗,YanWang2,AlanYuille21KeyLaboratoryofSpecialtyFiberOpticsandOpticalAccessNetworks,ShanghaiUniversity2DepartmentofComputerScience,JohnsHopkinsUniversitywei.shen@t.shu.edu
2、.cn,{wangbin418,jy9387}@outlook.com,{wyanny.9,alan.l.yuille}@gmail.comAbstractInthefieldofconnectomics,neuroscientistsseektoi-dentifycorticalconnectivitycomprehensively.NeuronalboundarydetectionfromtheElectronMicroscopy(EM)im-agesisoftendonetoassisttheautomaticreconstructionofneuronalci
3、rcuit.ButthesegmentationofEMimagesisachallengingproblem,asitrequiresthedetectortobeable(a)(b)(c)todetectbothfilament-likethinandblob-likethickmem-Figure1.Neuronalstructuresegmentation:anEMimage(a)andthegroundtruthsforitsneuronalboundarydetectionresult(b)andbrane,whilesuppressingtheambig
4、uousintracellularstruc-segmentationresult(c),respectively.ture.Inthispaper,weproposemulti-stagemulti-recursive-inputfullyconvolutionalnetworkstoaddressthisproblem.Themultiplerecursiveinputsforonestage,i.e.,themulti-riousandevenimpractical[13],whichdrivesthedemandplesideoutputswithdiffe
5、rentreceptivefieldsizeslearnedforefficientautomatedneuronalcircuitreconstructionap-fromthelowerstage,providemulti-scalecontextualbound-proaches.aryinformationfortheconsecutivelearning.Thisdesignisbiologically-plausible,asitlikesahumanvisualsystemSerialsectionEMproducesastackof2Dimagesbyc
6、ut-tocomparedifferentpossiblesegmentationsolutionstoad-tingsectionsofbraintissue.Duetotheanisotropicresolu-dresstheambiguousboundaryissue.Ourmulti-stagenet-tionsofin-planeandout-of-plane,mostneuronalcircuitre-worksaretrainedend-to-end.Itachievespromisingre-constructionapproachesfollowt
7、hefollowingpipeline:(1)sultsontwopublicavailableEMsegmentationdatasets,neuronalboundarydetectiononeach2Dimage,(2)neu-themousepiriformcortexdatasetandtheISBI2012EMronalstructuresegmentationbasedonthe2Dboundarydataset.map,and(3)linkingtheneuronalsegmentsacross2Dim-agesintoa3Dreconstruc