from face detec,on to the faces of scien,fic images€¦ · 16/06/2016 · from face detec,on to...
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Fromfacedetec,ontothefacesofscien,ficimages:
ScalingAnaly,csforImageDatafromExperiments
LawrenceBerkeleyNa.onalLaboratory,Berkeley,CA,USA
DaniUshizima,HarinarayanKrishnan,TalitaPerciano,DulaParkinson,PeterErcius,WesBethelandJamesSethian
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DataAnaly,cs&Visualiza,onDAVGroup
Wes Bethel, Daniela Ushizima, Gunther Weber, Dmitriy Morozov, Hank Childs, Talita Perciano, Mark Howison, Oliver Ruebel, Burlen Loring, David Camp, Hari Krishnan
2
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Collaborators
CustomUI
DomainProcessing
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Embedded
Lightweight,Collabora,on
TailoredVis
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ClimateScience• Customizeuserinterfaces:
– Interface–Lat/Long2DGrid,3Dglobe,Con,nentalOverlays.– Op,ons–ZonalMeanAverages,ExtremeValues,PeaksOverThreshold,etc..
• Collabora,onandProvenance:– Collaborate,Control,&CommunicateresultwithpeersRecordandrecreateworkflows.
• ExtendCapabili,es:– ExtendExtremeValueAnalysisorPeaks-Over-Thresholdalgorithmorwritecustomanalysisrou,nesto
exploredata.
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DataBrowser(SDM,ACS),DeepVadoseZone(PNNL),JohnPeterson,SusanHubbardEnvironmentalScience
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Astrophysics(YT),Climatescience(R),VTK(python)
Astrophysics
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ImageProcessing,Reconstruc,on,Segmenta,on,andAnalysis
Restofthetalk…
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Overview1. Inves,ga,ngimage-basedexperiments:
a. MaterialScience-focusedimageanalysis;
b. Health-focusedimageanalysis;
2. Computermethodsandresults;
3. Scalingthroughpartnerships;4. Imageintheexascalelandscape
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OURTOOLS
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CAMERACenterforAppliedMathema,csforEnergyResearchApplica,ons
FractureAnalysisofHigh-resImages 10
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Nanoparticle Ocular fundus Head CT Radar image
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SAIDE projects – from nano to meter scale
SIAM2010
ISBI’14+15
AdvancedFunc.Materials2015
UXMagazine2013 PSOC-NCI2011
Real->meImagingActaMicroscopica ACS2014
DemoNCEM2013 DemoESD2012
DemoLSD2013
DemoEETD
IEEEBigData2014
Chemical+
Electronic+
Structural
Chemical+
Structural StructuralChemical
+Structural
Chemical+
Structural
Chemical+
Structural
Chemical+
Structural
Chemical+
StructuralStructural Structural
Chemical+
Structural
Chemical+
Structural
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ImageAcrossDomains
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Specimens• Materials,composites,compoundsandbiologicalsamples.
Formats• Tiff,jpeg,hdf5,featurevectors,mul,-resolu,onpyramids,binaries.
DataAnalysis• Morphometry;• Spectralcontent;
• Mul,modal;• Templates.
DataUnderstanding• Clustering;• Classifica,on;• Randomizedschemes;
• Visualiza,on.
Reproducibleresearch• Datarepositories;• Sokwarerepositories;
• Collabora,on.
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1.Imageanalysis@UCB-BIDS/LBL
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Geologicalsamples
Resistantcomposites
Free-sokware,open-source,git,reproducible
Cervicalcells
Pilliden,fier
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1.a.Imageanalysis@LBL/UCB-BIDS
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Geologicalsamples
Resistantcomposites
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ECRP,CAMERAandDAV
• Scidac2012– Geologicalsamples– Carbonsequestra,on
• MathFoundry2013– MicroCT-imagedsamples– ConfocalandPS-OC
• CAMERA2014– ASCR+BES
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rock
bone
composite
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The science question: material resilience sample (CMC) and instrument (microCT) ▪ DetectcracksandfiberbreaksfrommicroCTimagesfromALSto
quan.fytherobustnessandresilienceofnewmaterials:noautomatedmethodsexistforthistypeofanalysis;
▪ Constraints:(1)exis,ngsokwaretoolsincapableofmee,ngthroughputrequirementsandscaletofull-resolu,onoftheexperiment(raw~60GB)(2)unabletoprovidereal-,mefeedback.
WorkwasperformedatLawrenceBerkeleyNa,onalLaboratorybytheCRDCenterforAppliedMathema,csinEnergyResearchApplica,ons(CAMERA)andonALSBeamline8.3.2.Opera,onoftheALSissupportedbyU.S.DepartmentofEnergy,OfficeofBasicEnergySciences.CAMERAissupportedbyjointlybyU.S.DepartmentofEnergy,
AdvancedScien,ficCompu,ngResearchandOfficeofBasicEnergySciences.
t
Pressure & temperature
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Micro-CT Pattern RecognitionProblem: quantify micro-structural damage of ceramic matrix composites using time-resolved data for full exploration of the micro-tomographycontent; Goal:- Iden,fymaterialfailureanddeformi,esfrommicro-CT,forexample,to
inspectfiberreinforcedCMC,anddendritespermea,ngbaseries;- Real-,mefeedbackaboutdatacollec,onandsamplecondi,on;
Approach: • Develop scalable pattern
recognition algorithms to find defects from 3D images;
• Createsokwaretoolstobeserinterfacehumanstoinstrumentswithhighresolu,onhigh-throughput.
19
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DOEEarlyCareerResearchProject:ScalingAnaly.csforImageDatafromExperiments(SAIDE)D.Ushizima(P.I.),T.Perciano,H.Krishnan,D.Parkinson(ALS),R.Richie(UCB),E.W.Bethel(LBNL)&J.Sethian(CAMERA)
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FractureAnalysisofHigh-resImages 20
93N 133N 151N
Deformation evolution
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FractureAnalysisofHigh-resImages
approachTemplate matching
Apply F3D filtersto improve contrast
to extract compositeApply F3D filters
Template matching
Intersection with"Base Result"
with high tolerance
Template matching with low tolerance
Union
For each slice in the stack Prototype examples
Identification of structures
Rawdata
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Templatematching
FractureAnalysisofHigh-resImages 22
MSE(x, y) = 1n
[p(i, j)− f (x + i, y+ j)]2i, j∑
NCCC(x, y) =p(i, j)− p(i, j)
i, j∑ f (i, j)− f (i, j)
i, j∑
( p(i, j)− p(i, j)i, j∑ f (i, j)− f (i, j)
i, j∑ )2
#
$%%
&
'((
12
1)Similaritybetweenprototypesandlocalregions:
2)Determinethebestmatches:
approachTemplate matching
Apply F3D filtersto improve contrast
to extract compositeApply F3D filters
Template matching
Intersection with"Base Result"
with high tolerance
Template matching with low tolerance
Union
For each slice in the stack Prototype examples
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F3Dplugin• Accelerate key image
processingalgorithms• Enablesegmenta,onand
analysisofhighresolu.onimagedatasets
• Requirement:parallel-capablealgorithmstoaccommodatelargedatasizesandtoallowreal-,mefeedback
hVps://github.com/CameraIA/F3D
• Non-linearedgepreservingfilters• Morphologicaloperatorswithvaryingstrel
Image processing at high-resolution
DOEEarlyCareerResearchProgram
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FractureAnalysisofHigh-resImages 24
Quantitative results
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FractureAnalysisofHigh-resImages 25
17Xfaster
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4060
80
Performance
Data Size (Gb)
Tim
e (m
in)
0 2 5 7 12 19 30
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F3DF3D Virtual StackSacha
Performance evaluation: comparison between proposed filter and only tool previously available in Fiji
• IntelXeonCPUE5-2660-20GHz• 3NVIDIATeslaK20X+1K40m
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Terabyte-sizeimagerepresenta,on• Problem:
– Largedatasets(originally16GBperframe)• Solu,on:
– Mul,resolu,onpyramidsatfourdifferentscalesstoredasHDF5chunkedmul,-dimensionalarraysthroughBig-DataViewer;
– Pluginoriginallyoffersinterac,vearbitraryvirtualreslicingofmul,-terabyterecordings,sothattheusercaninspecttheexperimentaldataefficiently;
– Compressfilesandallowencapsula,onofterabyte-sizeimagedatasets,includingmetadata,andop,mizedaccesstomul,plescalesofthedata,bothforvisualiza,onaswellasforprocessing.
– OtheradvantagesofBigDataViewerformaung:a)increasedcompu,ngperformance,b)decreasedcluseringoftheexperimentalarchives,andc)poten,alforparallelI/O.
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Ref:T.Pietzsch,S.Saalfeld,S.Preibisch,andP.Tomancak.Bigdataviewer:visualiza,onandprocessingforlargeimagedatasets.NatureMethods,2015.
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Tes,ngfileswithdifferentsizes
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Scalabilityofthemul,-dimensionalrepresenta,onusingHDF5withincreasingdatasize.
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Advanced technique: team work
DOEEarlyCareerResearchProgram
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Inven,ngnewcodesforcharacteriza,onofreinforcedcomposites
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SignificanceandimpactScien=ficAchievement§ Analysisofthinfilmsbyusingscanning
transmissionelectronmicroscopy(STEM)tomographyimagesinsupportofmaterialarchitectureenhancement;
§ Quan,fyporestructureevolu,oninordertocontrolqualityoffabricatedfilms.
§ ResultsusingporosimetryfromSTEMimagescorroboratediniden,fica,onoffabrica,oncondi,onsthatledtothelowesteverdielectricconstantsfortheneededfilms.
§ Collabora,onwithIntel,LBLNCEMandOrganicandMacromolecularSynthesisattheMolecularFoundry,andSLAC,
Researchdetails*§ ReportedlowesteverdielectricconstantsforPMOmatrix
material,usedinmicroelectronics;§ Textureanalysisusingsecond-ordersta,s,csofimageintensity
varia,onstomeasurefilmroughness;§ Newtoolsadaptedto3DstacksforNCEMinstruments;§ Newdevelopments:porosityanalysisusingnewmaterial
architecturedrivers(withT.WilliamsandB.Helms)andspectralanalysisofcataly,cprocesses(withK.Bus,lloandP.Ercius).
Ref:Willsetal,“BlockCopolymerPackingLimitsandInterfacialReconfigurabilityintheAssemblyofPeriodicMesoporousOrganosilicas”,FuncionalMaterials2015.
Imageanalysisforqualitycontrolofmaterialarchitecture
Image-based porosimetry for quality control during assembly of films CAMERA and Molecular Foundry
*WorkwasperformedatLBNLbytheCRDDAVandCAMERA.DAVissupportedbyASCRandCAMERAjointlybyASCRandBES.
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FeaturedesignforSTEMimagedata
(A)(bluetraces)and(B)(redtraces).
DOEEarlyCareerResearchProgram
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Finalremarks
• Scalingthroughpartnerships:
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Finalremarks
• Algorithmsinanexascalelandscape– I/Oawareness
– Datareduc,onandin-situanalysis
– Machinelearning
– Experimental/observa,onaldatasets
– Digitaltwin
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