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科技英语阅读与写作

题目：CellSegmentationinDigitalHolographic

Images

学院：____________电子工程学院_________

专业：信号与信息处理

姓名：_________________________________

学号：_____________1602120898___________

Partner:

姓名：_______________张润东_____________

学号：1601120188

CELLSEGMENTATIONINDIGITALHOLOGRAPHICIMAGES

NohaEl-Zehiry'OliverHayden2andAliKamen1

,MedicalImagingTechnologies,SiemensHealthcare,Princeton,NJ,USA

2In-VitroDXandBioscience,SiemensHealthcare,Erlangen,Germany

ABSTRACT

DigitalHolographicMicroscopy(DHM)isbecomingrecentlyverypopularfor

cellimaging.①(1)Themainadvantageofdigitalholographicmicroscopyover

classicalmicroscopytechniquesisthatitdoesnotonlyprovidetheprojectedimageof

theobjectbutalsoprovidesthreedimensionalinformationoftheobject'soptical

thickness.DHMtechnologycouldbethecoreofalabel-freeimagingfbrhematology

applications.Inanidealframework,abloodsamplecanbeimagedusingDHM,

machinelearningapproachescan(beusedfor)thecellextraction,differentiationand

consequentlycomputingalltherelevantbloodstatisticssuchastheMeanCorpuscular

Volume(MCV),theRedBloodCell(RBC)count,RedBloodCellDistributionWidth

(RDW)._Themostvitalcomponentinsuchaframeworkisaccurateextractionofthe

cells.(2)(2)Thispaperpresentsanovelapproachtocellsegmentationinwhicha

probabilisticboostingtreeclassifieristrainedtodetectthecentersofthecellsusing

Haar-Featu】es・Thedetectedcellcentersareusedtotriggeramarker-controlledpower

watershedsegmentationtocomputethecellboundaries.Additionally,wepresenta

comprehensiveevaluationofsegmentationmethodsfbrcellextractionindigital

holographicimages.

1.INTRODUCTION

DigitalHolographicMicroscopy(DHM)hasreceivedalotofattentionrecently

incellimaging[1,2,3].(3)DHM,ifassociatedwithproperimageprocessing

algorithms,canserveasthefundamentalbuildingblockinalabelfreecelldiagnostics

workflow.Insuchaworkflow,accuratecellextractionisavitalsteptoperformthe

analysis.Therefore,thoroughinvestigationofcellsegmentationinDHMimagesisa

persistentnecessity.CurrentDHMapplicationstudiessuchas[1,2]usesimple

segmentationtoolsorutilizegenericcellsegmentationtools[4](that)arenot

designedforDHMandtheaccuracyoftheirperformanceforDHMwasnotassessed

intheliterature.Onenotableexceptionisthecellsegmentationapproachpresentedby

Yietal.in[3].Thealgorithmin[3]usesasequenceofmorphologicaloperationson

thephaseimagetogeneratemarkersformaker-controlledwatershedsegmentation.

Theapproachisrelativelycomplicatedandthemaindisadvantageisitssensitivityto

theparametersofthemorphologicaloperatorssuchasthesizeofthestructure

elementusedineveryoperation.Wewillovercomethisproblem.Inthispaper,we

presentatwo-stepsegmentationapproach:Celldetectiontolocalizethecentersofthe

cellsandcellsegmentationtodelineatetheboundariesofthecells.Qualitativeand

quantitativeassessmentofoursegmentationmethodispresented.Moreover,we

introduceacomparisontothestate-of-theartcellsegmentationmethods[3]and[4].

⑷Therestofthepaperisorganizedasfollows:Section2presentsthedetailsof

ourproposedsegmentationmethods,section3introducestheexperimentalresultsand

thecomparisontoothersegmentationmethods,thenaconcludingdiscussionis

Fig.I.PipelineofCellExtractionAlgorithm.Left:originalimagesuperimposedbytheprobabilitymapofapixel

beingacellcenter.Middle:Resultofaggregationoftheprobabilitymapresponseandgeneratingtheinternal

markers.Left:Segmentationresults.

2.METHODS

Markercontrolledwatershedhasbeenusedrepeatedlyforcellsegmentation[4,3,

5,6].Robustmarkergenerationisnecessarytoobtainqualitysegmentationresults.

Mostoftheprevioussegmentationmethods[3,4]useasequenceofmorphological

operationstoidentifythebestpossiblesetofmarkers.Onekeyproblemwithsuch

approachesis(that)itrequiresthetuningofmanyparametersforthestepswithinthe

morphologicaloperationchain.@(5)Itislesslikelytohavedsinglesetof

parametersthatcouldworkefficientlyforalltheimages.Ontheotherhand,tuning

theparametersforeachsingleimageiscompletelyimpracticalandnegatesthehigh

throughputadvantageassociatedwithDHMtechnology.Motivatedbytheprevious

drawbacks,wepresentanewcellsegmentationapproach.Thenoveltyofour

approachistwo-fold:First,robustmarkergeneration(using)cellcenterdetection.0

⑹Second,weusepowerwatershedforthecellsegmentationwhichhasbeenproven

moreefficientthanconventionalwatershedingeneralsegmentationproblems[7].

Figure1showsthepipelineofourcellextractionapproach.

2.1.RobustMarkerGeneration

(7)Weconsiderthemarkergenerationproblemasanobjectdetectionproblem

whereweaimatfindingthepositionsofcellcenters.Forthispurpose,weusea

machinelearningbasedapproachinsteadofmorphologicaloperationstominimizethe

sensitivitytoparameters9choice.Inthiscontext,weuseProbabilisticBoostingTree

(PBT)learningframework[8].Inthelearningphase,thePBTconstructsatree(in

which)eachnodecombinesasetofweakclassifiersintoastrongclassifier.Intesting

phase,theconditionalprobabilityiscomputedateachnodeandtheprobabilityis

propagatedtothesourceofthetreetoprovidetheoverallprobability.Inourtraining,

weusetheHaarfeatures[9]toformtheweakclassifiers.®⑻Intesting,we

computetheprobabilityofeachpixelintheimagebeingacenterofacell,the

probabilitymapisthresholdedtokeeponlythepixelsthataremorelikelytobeacell

center.

2.2.AggregationoftheDetectionResponses

Aftercomputingtheprobabilitymapandapplyingthethreshold,wegetahigh

responseinsideeachcell.However,theresponseisnotnecessarilysmoothand

connectedwhichmayleadtofalseidentificationofonecellasmultiplecells.

Therefore,toaggregatetheseresponse,weapplyaclusteringsteponthethresholded

probabilitymap.Theclusteringservestwopurposes,first,itaggregatedthecell

responsesinasinglecell.Second,itprovidesalargersetofpixelstoformasthe

internalmarkerforthesegmentation.Thepixelsofeachclusteraremergedintoa

singleconnectedcomponentthatservesasaninternalmarkerforthecell

segmentationstep.Themiddle

imageinFigure1showsasampleofthemarkerscomputedafterclustering.Theseare

usedasinternalcellmarkers,externalcellmarkershighlightingthebackgroundare

obtainedbyapplyingwatershedtransformontheinternalmarkers.

2.3.CellSegmentationusingPowerWatershed

Thepowerwatershedsegmentationreviewedinthissectionwaspresentedby

Couprieetal.in[7].Theformulationisperformedonadiscretegraph.Agraph

g={V,E}consistsofasetofverticesvEVandasetofedgeseG£QVxV.An

edgeincidenttovertices片andvjisdenotedey.Inourformulation,eachpixelis

identifiedwithanode,片.Aweightedgraphisagraphinwhicheveryedgee,is

assignedaweight

w.⑼Theseededsegmentationenergyin-7]wasgivenas

ngnE啕叫一叼/+£共产?+£呜皿一1R

s.t.x(F)=1.x(B)=0.

s,=1ifX,>I，0ifXa<(1)

wherex^and与arethebinarylabelsassociatedwithvertices,andrespectively.F

andBrepresentthesetsofforegroundandbackgroundmarkers.(Itwasshownin[7]

that)whenpooandq>1,thisleadstoamoregeneralwatershed,namely,power

watershedthatyieldedbetterresults.Cellsegmentationcouldbenefitfbrthe

improvementsassociatedwithpowerwatershed.InSection3,wewillpresentthe

comparisonbetweenthesegmentationresultsusingpowerwatershed!andwatershed

thathasbeenusedinthevastliteraturefbrcellsegmentation.

DonorDonorDataDonorOverall

12Set34

TP97.4%97.8%97.3%96.5%97.2%

FP6.8%4.3%3.7%5.7%5%

Table1.Resultsofthedetectionofthecellcenter.TPandFParethetruepositiveandfalse

positiverates,respectively.

3.EXPERIMENTALRESULTS

Inadditiontothenovelsegmentationapproach,weconsidertheevaluationand

comparisonofsegmentationmethodsforDHMasamajorcontributionofthepaper.

Wechoseasubsetofsegmentationmethodsthatweconsiderrepresentativeofthe

currentsegmentationmethods.Qualitativeandquantitativecomparisonwillbe

presentedinthissection.

3.1.DataDescriptionandCellDetection

ThedatasetisacquiredusingtheQModHolographicandFluorescence

Microscope[10].Thecellswereilluminatedwithalightsourceofwavelength=

550nmandthemagnificationobjectiveis60x1.Thenumberofdonorsusedinthis

studyis4.Thenumberofimagesforeachdonor(variesfrom)6-9imageswithatotal

of28images.Eachimagecontainsmultiplecellsvaryingfrom10-30withatotalof

615cells,lbevaluatetheaccuracyofthedetectionalgorithm,weusen-foldleaveone

outcrossvalidation.Specifically,wedid4training/testingsets.Foreachsetweleave

outalltheimagesassociatedwithagivendonorandtraintheclassifierontheimages

oftherestofthedonors.Fortesting,wetestonlyontheunseendatafromtheleftout

donor.(10)Weexcludevllthecellsthataretouchingtheboundariesoftheimageto

ensurethattheevaluationisperformedonlyonvalidcellcandidates.

Theprobabilisticboostingtreeoutputsaprobabilitymapofeachpixel(being)a

cellcenter.Wethresholdtheprobabilitymapatp=0:75toconsideronlythepixels

thatarehighlikelytobecells.(11)Thethresholddoesnotprovideasinglecellcenter

butratheraclusterofpointsinthecenterofthecellasdepictedinthefirstimageof

Figure1.Inthisimage,theprobabilitymapissuperimposedontheoriginalimage

withthelostprobabilityinredandthehighestprobabilityinblue.Table1showsthe

(12)resultforthedetectionsystem.

3.2.CellSegmentation

Wechosearepresentativesubsetofcellsegmentationmethodstocompare

againstMl[4]andM2[3].Moreover,todecoupletheeffectofeachcontribution(the

machinelearningdetectioncomponentandthepowerwatershedsegmentation

component),wepresenttwonovelmethodsM3thatbenefitsfromdetection

componentonlyandM4thatbenefitsfromthepowerwatershedsegmentation

componentonly.ThedescriptionsofmethodsMl-M5aresummarizedasfollows:

l.CellProfiler[4](Ml):CellProfiler(isconsideredas)oneofthebenchmarksin

cellsegmentationandhasbeenrepeatedlyusedbyotherresearchgroupsto

obtainthesegmentationandprovideanalysis.

2.MarkerControlledWatershedforDHM(M2)[3]:Dedicatedcellsegmentation

forDHMtechnologywasonlydiscussedin[3].Themethodusesa

complicatedworkflowtogeneratethemarkersforthewatershedsegmentation.

Theworkflowissummarizedasfollows:(1)Imageisnormalized,(2)Otsu's

thresholdisappliedtoobtainIbin,(3)Holesarefilledusingmorphological

constructiontoIbin,(4)GradientimageIgradiscomputedusingSobel

operator.(5)MorphologicalopeningisappliedtoIbintoobtainlopen.(6)

Morphologicalerosion(13)isappliedtolopentoobtainlerode.(7)

Morphologicalreconstructionisappliedwithlopenasthemaskandlerodeas

amarkertoobtain\rec.(8)ComputeIsubasIopen-Irec.(9)Apply

morphologicaldilationonlerodetoobtainIdilate(10)Obtaintheinternal

markersbycombiningIsubandIdilate.(11)Applywatershedtransformonthe

internalmarkerstogeneratetheexternalmarkers.(12)Applymarkercontrolled

watershedsegmentationusingthemarkersgeneratedin(11).Mostofthe

previousstepsrequiretuningofstructureelementparameterswhichmakesthe

workflowerrorprone.Althoughtheworkflowwascarefullycraftedwith

specificparametersprovidedbytheauthorsin[3]toavoidmergingtouching

cellsoreliminatingsmallcomponents,asinglesetofparametersdoesnotwork

wellinallscenarios.Wetestedseveralcombinationsofparameterstotryto

achievethebestresultsforourdataset.Afterseveralexperiments,wefigured

thattheparametersprovidedbytheauthorsin[3]workbest.(14)Hence,for

thecomparison,weusetheparameterselectionin⑶.Onecanarguethatthe

improvementover[3]isduetoperformingthesegmentationusingpower

watershedratherthanthegenerationoftheaccuratemarkers.So,(15)itis

worthclarifyingthattheimprovementisduetoacombinationofbothfactors.

Todecoupletheeffectofeachcomponent.Wealsocompareagainsttwoother

workflows(M3andM4).

3.MachineLearningMarkerGenerationwithWatershed(M3):Inthismethod,

weusethemarkersgeneratedbythemachinelearningbasedcellcenter

detectionto

triggerwatershedsegmentation.

4.MorphologicalMarkerGenerationwithPowerWatershed(M4):Inthismethod,

wegeneratethemarkersusingthemorphologicalworkflow(M2)andapply

powerwatershedforthesegmentationinsteadofwatershed.

5.MachineLearningMarkerGenerationwithPowerwatershed(M5):Thisisthe

proposedworkflowwhichtakesadvantageofrobustmarkergenerationaswell

asmoreaccuratesegmentationusingpowerwatershed.

Figure2showsasampleofourresults.Thegallerydepictstheresultsofthefive

algorithmsforthreedifferentimagesfromdifferentdonors.Thethirdcolumnshows

thatallthealgorithmsworkcomparablywellifthecellsaresparselydistributedinthe

image.This,however,isnotverypracticalasitdoesnotefficientlyutilizethefull

fieldofview.Inpractice,thecellsmaybeveryclosetoeachother.Insuchscenarios,

mostofthemorphologicalbasedapproacheswouldfailinaccuratelyextractingthe

cells.Intheyellowboxes,weshowanexamplewheretwocellsaretouching.The

algorithmin[4]mergesthetwocellsinoneentity.Thecarefullycrafted

morphologicalworkflowin[3]managedtoseparatethetwocellsasshowninthe

secondrowand(16)sodidallthealgorithmswedevelopedasshowninthethird,

fourthandfifthrows.Whenthecellsslightlyoverlaps,itbecomesmorechallenging

andmorphologicalbasedmethodsfailtoaccuratelyextractthecellsasshowninthe

examplesinthegreenboxes.Ontheotherhand,M4andM5cansuccessfullyextract

thecellsduetotherobustnessofthecellcenterdetectioncomponent.Itisworth

notingthathighthroughputsystemsrequireafullutilizationofthefieldofview

whichresultsinmultipleoccurrencesofsuchtouchingorslightlyoverlappingcells.A

limitationofthecurrentapproachisthatwhentheoverlapbetweenthecellsislarge,

theboundariescannotbedelineatedaccuratelyasdepictedintheblueboxes.However,

eveninsuchscenario,ouralgorithmismoreaccuratethan[4]and[3]becauseitstill

identifiedtwocellswhichisimportantforan

accuratecellcount.

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Fig.2.Sampleofthesegmentationresultsobtainedusingthefivedifferentalgorithms.

Forquantitativeassessment,wechosethemostcommonmetricsfor

segmentationevaluation,namely,thesensitivity,specificity,DiceandJaccard

similarityindexdefinedas:

TP

Sensitivity

TP+F.V•

TN

Specificity⑵

TN+FP'

TTP

DiceSimilarityIndex

JaccardSimilarityIndex

TP+FP+N

whereTP,FP,TNandFNrefertothetruepositives,falsepositives,truenegativesand

falsenegatives,respectively.

Table2showstheresultsforthemethodsM1-M5.Thequantitativeassessment

showsthateachcomponentweintroduced(contributedto)improvingthe

segmentationresults.However,(17)itisevidentthattherobustlocalizationofthecells

usingthecelldetectionframeworkplayedamorecrucialrole.Whilethesensitivity

improvedby10%whenusingourmachinelearningmarkergeneration,itonly

improved2%whenusingpowerwatershedinsteadofwatershed.

M1M2M3M4M5

Sensitivity88.7083.3792.9484.5894.83

Specificity98.1898.5797.4498.9298.44

Jaccard80.9577.5581.5380.0686.51

Dice89.3288.8489.7488.8492.66

Table2.QuantitativeComparisonbetweenthedifferentSegmentationMethods.

4.CONCLUSIONANDFUTUREWORK

Thepaperpresentedanovelapproachforcellsegmentationindigital

holographicmicroscopicimages.Therobustmarkergenerationpresentedinthepaper

outperformsthemorphologicalworkflowformarkergenerationthatiscommonly

usedtoinitiatewatershedsegmentation.Weintroducedthepowerwatershedtothe

cellsegmentationproblemandpresentedquantitativeevidencethatitworksbetter

thanwatershed.Thepaperpresentedaquantitativecomparisonoftheproposed

approachtothecommonmethodsusedforcellsegmentation.Onelimitationofour

approachandallthemethods(M1-M5)isthatitisnotcapableofproperlysegmenting

overlappingcells.Inthefuture,weplantoaddpostprocessingthatidentifiesthe

overlappingcellsandformulatesalayeredsegmentationalgorithmtoseparate

properlytheoverlappingcells.

一、语法分析

(1)Themainadvantageofdigitalholographicmicroscopyoverclassicalmicroscopy

techniquesisthatitdoesnotonlyprovidetheprojectedimageoftheobjectbut

alsoprovidesthreedimensionalinformationoftheobject'sopticalthickness.

本句主语为Themainadvantage,ofdigitalholographicmicroscopy作为后置定

语修饰主语，advantage...over为固定搭配表示与…相比的优势含义，表语由

that引导的表语从句充当，其中notonly…butalso…表示不仅而且的意思，并

且of+n具有形容词的含义。

(2)Thispaperpresentsanovelapproachtocellsegmentationinwhichaprobabilistic

boostingtreeclassifieristrainedtodetectthecentersofthecellsusing

Haar-Features.

本句主句为Thispaperpresentsanapproach.present:提出，approachto…：固定

搭配，…的方法，inwhich引导定语从句修饰approach,这时可与where互换，

classifieristrainedtodetect…不定式表目的，thecentersofthecellsusing

Haar-Features.using前省略的介词by,表方式。

(3)DHM,ifassociatedwithproperimageprocessingalgorithms,canserveasthe

fundamentalbuildingblockinalabelfreecelldiagnosticsworkflow.

ifassociatedwithproperimageprocessingalgorithms在句中作条件状语，可将

其放在句首：Ifassociatedwithproperimageprocessingalgorithms,DHMcan

serveasthefundamentalbuildingblockinalabelfreecelldiagnosticsworkflow.

连词+分词做状语，省略了主语(If也可省略)，主语是主句主语，serveas为

固定短语表示充当…，inalabelfreecelldiagnosticsworkflow介词短语做状语。

(4)Therestofthepaperisorganizedasfollows:Section2presentsthedetailsofour

proposedsegmentationmethods,section3introducestheexperimentalresultsand

thecomparisontoothersegmentationmethods,thenaconcludingdiscussionis

providedinsection4.

该段主要是对文章结构进行简要说明，thepaperisorganizedasfollows采用了

organize的被动态以及短语asfollows,为避免重复，对每一部分的介绍使用了

不同动词，分另II是present,introduce,provide；thecomparisontoother

segmentationmethods.comparisonto为compareto…的变形;引入最后一部分

使用了连词then而不是finally.

(5)Itislesslikelytohaveasinglesetofparametersthatcouldworkefficientlyforall

theimages.

Itislikelytodo…表示做什么事很有可能,less含义与more相反,Itislesslikely

todo…表示做…不太可能，it为形式主语，真正主语为不定式。asetof:一套，

一副，that引导一个定语从句修饰parameters,work表示起作用，工作的意

思。

(6)Second,weusepowerwatershedforthecellsegmentationwhichhasbeenproven

moreefficientthanconventionalwatershedingeneralsegmentationproblems[7].

Second:其次；weusepowerwatershedforthecellsegmentation,use…for…:将...

用在…上；which引导定语从句修饰powerwatershed,句中使用现在完成时

的被动态hasbeenproven表示此事现在已经被证明了；moreefficientthan…比

较级的运用，表示比…更有效。

(7)Weconsiderthemarkergenerationproblemasanobjectdetectionproblemwhere

weaimatfindingthepositionsofcellcenters.

consider…as…：将…视为…;where引导定语从句可将其替换成inwhich(如

(2));aimat*-：旨在…;of+n.做后置定语。

(8)Intesting,wecomputetheprobabilityofeachpixelintheimagebeingacenterof

acell,theprobabilitymapisthresholdedtokeeponlythepixelsthataremore

likelytobeacellcenter.

Intesting:在测试中，做状语；ofeachpixel…做后置定语修饰probability,each

pixelbeingacenterofacell是分词的独立结构，分词带有自己的主语；the

pixelsthataremorelikelytobeacellcenter