From face detec,on to the faces of scien,fic images€¦ · 16/06/2016 · From face detec,on to...
Transcript of From face detec,on to the faces of scien,fic images€¦ · 16/06/2016 · From face detec,on to...
Fromfacedetec,ontothefacesofscien,ficimages:
ScalingAnaly,csforImageDatafromExperiments
LawrenceBerkeleyNa.onalLaboratory,Berkeley,CA,USA
DaniUshizima,HarinarayanKrishnan,TalitaPerciano,DulaParkinson,PeterErcius,WesBethelandJamesSethian
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
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Collaborators
CustomUI
DomainProcessing
Embedded
Lightweight,Collabora,on
TailoredVis
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.
DataBrowser(SDM,ACS),DeepVadoseZone(PNNL),JohnPeterson,SusanHubbardEnvironmentalScience
Astrophysics(YT),Climatescience(R),VTK(python)
Astrophysics
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ImageProcessing,Reconstruc,on,Segmenta,on,andAnalysis
Restofthetalk…
Overview1. Inves,ga,ngimage-basedexperiments:
a. MaterialScience-focusedimageanalysis;
b. Health-focusedimageanalysis;
2. Computermethodsandresults;
3. Scalingthroughpartnerships;4. Imageintheexascalelandscape
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OURTOOLS
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
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.
1.Imageanalysis@UCB-BIDS/LBL
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Geologicalsamples
Resistantcomposites
Free-sokware,open-source,git,reproducible
Cervicalcells
Pilliden,fier
1.a.Imageanalysis@LBL/UCB-BIDS
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Geologicalsamples
Resistantcomposites
ECRP,CAMERAandDAV
• Scidac2012– Geologicalsamples– Carbonsequestra,on
• MathFoundry2013– MicroCT-imagedsamples– ConfocalandPS-OC
• CAMERA2014– ASCR+BES
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rock
bone
composite
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
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.
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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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93N 133N 151N
Deformation evolution
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
Templatematching
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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
#
$%%
&
'((
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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
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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Quantitative results
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17Xfaster
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020
4060
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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
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.
Tes,ngfileswithdifferentsizes
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Scalabilityofthemul,-dimensionalrepresenta,onusingHDF5withincreasingdatasize.
Advanced technique: team work
DOEEarlyCareerResearchProgram
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Inven,ngnewcodesforcharacteriza,onofreinforcedcomposites
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.
FeaturedesignforSTEMimagedata
(A)(bluetraces)and(B)(redtraces).
DOEEarlyCareerResearchProgram
Finalremarks
• Scalingthroughpartnerships:
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Finalremarks
• Algorithmsinanexascalelandscape– I/Oawareness
– Datareduc,onandin-situanalysis
– Machinelearning
– Experimental/observa,onaldatasets
– Digitaltwin
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