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Deep Learning for Medical Image Analysis
Edited by S. Kevin Zhou
Hayit Greenspan Dinggang Shen
DeepLearningfor MedicalImage Analysis
TheElsevierandMICCAISociety BookSeries
Advisoryboard
StephenAylward (Kitware,UnitedStates)
DavidHawkes (UniversityCollegeLondon,UnitedKingdom)
KensakuMori (UniversityofNagoya,Japan)
AlisonNoble (UniversityofOxford,UnitedKingdom)
SoniaPujol (HarvardUniversity,UnitedStates)
DanielRueckert (ImperialCollege,UnitedKingdom)
XavierPennec (INRIASophia-Antipolis,France)
PierreJannin (UniversityofRennes,France)
Alsoavailable:
Balocco,ComputingandVisualizationforIntravascularImagingand ComputerAssistedStenting,9780128110188
Wu,MachineLearningandMedicalImaging,9780128040768
Zhou,MedicalImageRecognition,SegmentationandParsing, 9780128025819
DeepLearningfor MedicalImage Analysis
Editedby S.KevinZhou
HayitGreenspan DinggangShen
AcademicPressisanimprintofElsevier
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PART1INTRODUCTION
CHAPTER1AnIntroductiontoNeuralNetworksandDeep Learning .......................................... 3
Heung-IlSuk
1.1 Introduction.........................................3
1.2 Feed-ForwardNeuralNetworks........................4
1.2.1Perceptron....................................4
1.2.2Multi-LayerNeuralNetwork.....................5
1.2.3LearninginFeed-ForwardNeuralNetworks........6
1.3 ConvolutionalNeuralNetworks........................8
1.3.1ConvolutionandPoolingLayer...................8
1.3.2ComputingGradients...........................9
1.4 DeepModels........................................11
1.4.1VanishingGradientProblem.....................11
1.4.2DeepNeuralNetworks..........................12
1.4.3DeepGenerativeModels........................14
1.5 TricksforBetterLearning.............................20
1.5.1RectifiedLinearUnit(ReLU)....................20
1.5.2Dropout......................................20
1.5.3BatchNormalization............................21
1.6 Open-SourceToolsforDeepLearning...................22 References..........................................22 Notes..............................................24
CHAPTER2AnIntroductiontoDeepConvolutionalNeural NetsforComputerVision ......................... 25 SurajSrinivas,RaviK.Sarvadevabhatla, KondaR.Mopuri,NikitaPrabhu, SrinivasS.S.KruthiventiandR.VenkateshBabu
2.1 Introduction.........................................26
2.2 ConvolutionalNeuralNetworks........................27
2.2.1BuildingBlocksofCNNs.......................27 2.2.2Depth........................................29
2.2.3LearningAlgorithm............................30 v
3.4.1AnatomyDetectionandSegmentationin3D........71
3.4.2LandmarkDetectionin2Dand3D................74
4.3.3LearningStageII:CNNBoosting.................90
4.3.4Run-TimeClassification.........................92
4.4 Results.............................................93
4.4.1ImageClassificationonSyntheticData............93
4.4.2Body-PartRecognitiononCTSlices..............95
4.5 DiscussionandFutureWork...........................99 References..........................................100
CHAPTER5AutomaticInterpretationofCarotidIntima–Media ThicknessVideosUsingConvolutionalNeural Networks .......................................... 105 NimaTajbakhsh,JaeY.Shin,R.ToddHurst, ChristopherB.KendallandJianmingLiang
5.1 Introduction.........................................106
5.2 RelatedWork........................................107
5.3 CIMTProtocol......................................109
5.4 Method.............................................109
5.4.1ConvolutionalNeuralNetworks(CNNs)...........109
5.4.2FrameSelection................................110
5.4.3ROILocalization...............................112
5.4.4Intima–MediaThicknessMeasurement............115
5.5 Experiments.........................................117
5.5.1Pre-andPost-ProcessingforFrameSelection.......118
5.5.2ConstrainedROILocalization....................118
5.5.3Intima–MediaThicknessMeasurement............121
5.5.4End-to-EndCIMTMeasurement..................123
5.6 Discussion..........................................124
5.7 Conclusion..........................................128 Acknowledgement...................................128 References..........................................128 Notes..............................................131
CHAPTER6DeepCascadedNetworksforSparselyDistributed ObjectDetectionfromMedicalImages ........... 133 HaoChen,QiDou,LequanYu,JingQin,LeiZhao, VincentC.T.Mok,DefengWang,LinShiand Pheng-AnnHeng
6.1 Introduction.........................................134
6.2 Method.............................................136
6.2.1CoarseRetrievalModel.........................136
6.2.2FineDiscriminationModel......................139
6.3 MitosisDetectionfromHistologyImages................139
6.3.1Background...................................139
6.3.2TransferLearningfromCross-Domain.............140
6.3.3DatasetandPreprocessing.......................140
6.3.4QuantitativeEvaluationandComparison...........141
6.3.5ComputationCost..............................142
6.4 CerebralMicrobleedDetectionfromMRVolumes.........143
6.4.1Background...................................143
6.4.23DCascadedNetworks.........................144
6.4.3DatasetandPreprocessing.......................146
6.4.4QuantitativeEvaluationandComparison...........147
6.4.5SystemImplementation.........................149
7.1 DeepVoting:ARobustApproachTowardNucleus LocalizationinMicroscopyImages.....................156
7.1.1Introduction...................................156
7.1.2Methodology..................................158
7.1.3WeightedVotingDensityEstimation..............162
7.1.4Experiments...................................163
7.1.5Conclusion....................................165
7.2 StructuredRegressionforRobustCellDetectionUsing ConvolutionalNeuralNetwork.........................165
7.2.1Introduction...................................165
7.2.2Methodology..................................166
7.2.3ExperimentalResults...........................169 7.2.4Conclusion....................................171
PART3MEDICALIMAGESEGMENTATION
JeffreyJ.Nirschl,AndrewJanowczyk,EliotG.Peyster, ReneeFrank,KennethB.Margulies, MichaelD.FeldmanandAnantMadabhushi
8.2.1DataSetDescription............................183
8.2.2ManualGroundTruthAnnotations................183
8.2.3Implementation................................183
8.2.4TrainingaModelUsingEngineeredFeatures.......185
8.2.5Experiments...................................186
8.2.6TestingandPerformanceEvaluation...............188
8.3 ResultsandDiscussion................................188
8.3.1Experiment1:ComparisonofDeepLearningand RandomForestSegmentation....................188
8.3.2Experiment2:EvaluatingtheSensitivityofDeep LearningtoTrainingData.......................188
8.4 ConcludingRemarks.................................191 Notes..............................................191 DisclosureStatement.................................191 Funding............................................192 References..........................................192
CHAPTER9DeformableMRProstateSegmentationviaDeep FeatureLearningandSparsePatchMatching .... 197 YanrongGuo,YaozongGaoandDinggangShen
9.1 Background.........................................197
9.2 ProposedMethod....................................199
9.2.1RelatedWork..................................199
9.2.2LearningDeepFeatureRepresentation.............201
9.2.3SegmentationUsingLearnedFeatureRepresentation.206
9.3 Experiments.........................................211
9.3.1EvaluationofthePerformanceofDeep-Learned Features......................................212
9.3.2EvaluationofthePerformanceofDeformableModel216
9.4 Conclusion..........................................219 References..........................................219
CHAPTER10CharacterizationofErrorsinDeepLearning-Based BrainMRISegmentation .......................... 223 AkshayPai,Yuan-ChingTeng,JosephBlair, MichielKallenberg,ErikB.Dam,StefanSommer, ChristianIgelandMadsNielsen
10.1 Introduction.........................................224
10.2 DeepLearningforSegmentation.......................225
10.3 ConvolutionalNeuralNetworkArchitecture..............226
10.3.1BasicCNNArchitecture.........................226
10.3.2Tri-PlanarCNNfor3DImageAnalysis............227
PART4MEDICALIMAGEREGISTRATION
CHAPTER11ScalableHighPerformanceImageRegistration FrameworkbyUnsupervisedDeepFeature RepresentationsLearning 245 ShaoyuWang,MinjeongKim,GuorongWuand DinggangShen
11.1
11.2.2LearnIntrinsicFeatureRepresentationsby UnsupervisedDeepLearning.....................250
11.2.3RegistrationUsingLearnedFeatureRepresentations.255 11.3 Experiments.........................................258
11.3.1ExperimentalResultonADNIDataset.............259
11.3.2ExperimentalResultonLONIDataset.............260
11.3.3ExperimentalResulton7.0-TMRImageDataset....263 11.4 Conclusion..........................................265
12.4 RegressionStrategy..................................276
12.4.1ParameterSpacePartitioning.....................276
12.4.2MarginalSpaceRegression......................277
12.5 FeatureExtraction....................................277
12.5.1LocalImageResidual...........................277
12.5.23-DPointsofInterest...........................279
12.6 ConvolutionalNeuralNetwork.........................280
12.6.1NetworkStructure..............................280
12.6.2TrainingData..................................281
12.6.3Solver........................................282
12.7 ExperimentsandResults..............................283
12.7.1ExperimentSetup..............................283
12.7.2Hardware&Software...........................285
12.7.3PerformanceAnalysis...........................286
12.7.4ComparisonwithState-of-the-ArtMethods.........288
12.8 Discussion..........................................292 Disclaimer..........................................294 References..........................................294
PART5COMPUTER-AIDEDDIAGNOSISANDDISEASE QUANTIFICATION
CHAPTER13ChestRadiographPathologyCategorizationvia
TransferLearning ................................. 299
IditDiamant,YanivBar,OferGeva,LiorWolf, GaliZimmerman,SivanLieberman,EliKonenand HayitGreenspan
13.1 Introduction.........................................300
13.2 ImageRepresentationSchemeswithClassical(Non-Deep) Features............................................303
13.2.1ClassicalFiltering..............................304
13.2.2Bag-of-Visual-WordsModel.....................305
13.3 ExtractingDeepFeaturesfromaPre-TrainedCNNModel..306
13.4 ExtendingtheRepresentationUsingFeatureFusionand Selection...........................................309
13.5 ExperimentsandResults..............................309
13.5.1Data.........................................309
13.5.2ExperimentalSetup.............................310
13.5.3ExperimentalResults...........................310
13.6 Conclusion..........................................315 Acknowledgements..................................317 References..........................................318
CHAPTER14DeepLearningModelsforClassifying
GustavoCarneiro,JacintoNascimentoand AndrewP.Bradley
15.5.1ParticipantDataandPreprocessing................360
PART6OTHERS
CHAPTER16DeepNetworksandMutualInformation MaximizationforCross-ModalMedicalImage Synthesis 381
RavitejaVemulapalli,HienVanNguyenand S.KevinZhou
16.1 Introduction.........................................382
16.2 SupervisedSynthesisUsingLocation-SensitiveDeep Network............................................384
16.2.1Backpropagation...............................386
16.2.2NetworkSimplification.........................387
16.2.3Experiments...................................388
16.3 UnsupervisedSynthesisUsingMutualInformation Maximization.......................................390
16.3.1GeneratingMultipleTargetModalityCandidates....392
16.3.2FullImageSynthesisUsingBestCandidates........393
16.3.3RefinementUsingCoupledSparseRepresentation...396
16.3.4ExtensiontoSupervisedSetting..................396
16.3.5Experiments...................................397
16.4 ConclusionsandFutureWork..........................401 Acknowledgements..................................401 References..........................................401 Note...............................................403
CHAPTER17NaturalLanguageProcessingforLarge-Scale MedicalImageAnalysisUsingDeepLearning .... 405 Hoo-ChangShin,LeLuandRonaldM.Summers
17.1 Introduction.........................................406
17.2 FundamentalsofNaturalLanguageProcessing ............407
17.2.1PatternMatching...............................407
17.2.2TopicModeling................................410
17.3 NeuralLanguageModels..............................411
17.3.1WordEmbeddings..............................411
17.3.2RecurrentLanguageModel......................412
17.4 MedicalLexicons....................................414
17.4.1UMLSMetathesaurus...........................414 17.4.2RadLex.......................................414
17.5 PredictingPresenceorAbsenceofFrequentDiseaseTypes.414 17.5.1MiningPresence/AbsenceofFrequentDiseaseTerms414 17.5.2PredictionResultandDiscussion.................415
17.6 Conclusion..........................................419
Contributors
R.VenkateshBabu IndianInstituteofScience,Bangalore,India
YanivBar
Tel-AvivUniversity,Ramat-Aviv,Israel
JosephBlair UniversityofCopenhagen,Copenhagen,Denmark
AndrewP.Bradley UniversityofQueensland,Brisbane,QLD,Australia
GustavoCarneiro UniversityofAdelaide,Adelaide,SA,Australia
HaoChen
TheChineseUniversityofHongKong,HongKong,China
ErikB.Dam
BiomediqA/S,Copenhagen,Denmark
IditDiamant
Tel-AvivUniversity,Ramat-Aviv,Israel
QiDou
TheChineseUniversityofHongKong,HongKong,China
MichaelD.Feldman
UniversityofPennsylvania,Philadelphia,PA,UnitedStates
ReneeFrank UniversityofPennsylvania,Philadelphia,PA,UnitedStates
YaozongGao UniversityofNorthCarolinaatChapelHill,ChapelHill,NC,UnitedStates
BogdanGeorgescu
SiemensMedicalSolutionsUSA,Inc.,Princeton,NJ,UnitedStates
OferGeva
Tel-AvivUniversity,Ramat-Aviv,Israel
FlorinC.Ghesu
SiemensMedicalSolutionsUSA,Inc.,Princeton,NJ,UnitedStates; Friedrich-AlexanderUniversityErlangen–Nürnberg,Erlangen,Germany
HayitGreenspan
Tel-AvivUniversity,Ramat-Aviv,Israel
YanrongGuo
UniversityofNorthCarolinaatChapelHill,ChapelHill,NC,UnitedStates
Pheng-AnnHeng
TheChineseUniversityofHongKong,HongKong,China
JoachimHornegger
Friedrich-AlexanderUniversityErlangen–Nürnberg,Erlangen,Germany
R.ToddHurst
MayoClinic,Scottsdale,AZ,UnitedStates
ChristianIgel
UniversityofCopenhagen,Copenhagen,Denmark
VamsiK.Ithapu
UniversityofWisconsin–Madison,Madison,WI,UnitedStates
AndrewJanowczyk
CaseWesternReserveUniversity,Cleveland,OH,UnitedStates
SterlingC.Johnson
WilliamS.MiddletonMemorialHospital,Madison,WI,UnitedStates; UniversityofWisconsin–Madison,Madison,WI,UnitedStates
MichielKallenberg
BiomediqA/S,Copenhagen,Denmark
ChristopherB.Kendall
MayoClinic,Scottsdale,AZ,UnitedStates
MinjeongKim
UniversityofNorthCarolinaatChapelHill,ChapelHill,NC,UnitedStates
EliKonen
ShebaMedicalCenter,Tel-Hashomer,Israel
SrinivasS.S.Kruthiventi
IndianInstituteofScience,Bangalore,India
JianmingLiang
ArizonaStateUniversity,Scottsdale,AZ,UnitedStates
RuiLiao
SiemensMedicalSolutionsUSA,Inc.,Princeton,NJ,UnitedStates
SivanLieberman
ShebaMedicalCenter,Tel-Hashomer,Israel
LeLu
NationalInstitutesofHealthClinicalCenter,Bethesda,MD,UnitedStates
AnantMadabhushi
CaseWesternReserveUniversity,Cleveland,OH,UnitedStates
KennethB.Margulies
UniversityofPennsylvania,Philadelphia,PA,UnitedStates
DimitrisMetaxas
RutgersUniversity,Piscataway,NJ,UnitedStates
ShunMiao
SiemensMedicalSolutionsUSA,Inc.,Princeton,NJ,UnitedStates
VincentC.T.Mok
TheChineseUniversityofHongKong,HongKong,China
KondaR.Mopuri
IndianInstituteofScience,Bangalore,India
JacintoNascimento
InstitutoSuperiorTécnico,Lisbon,Portugal
HienVanNguyen
UberAdvancedTechnologyCenter,Pittsburgh,PA,UnitedStates
MadsNielsen
BiomediqA/S,Copenhagen,Denmark; UniversityofCopenhagen,Copenhagen,Denmark
JeffreyJ.Nirschl
UniversityofPennsylvania,Philadelphia,PA,UnitedStates
AkshayPai
BiomediqA/S,Copenhagen,Denmark; UniversityofCopenhagen,Copenhagen,Denmark
EliotG.Peyster
UniversityofPennsylvania,Philadelphia,PA,UnitedStates
NikitaPrabhu
IndianInstituteofScience,Bangalore,India
JingQin
TheHongKongPolytechnicUniversity,HongKong,China
RaviK.Sarvadevabhatla
IndianInstituteofScience,Bangalore,India
DinggangShen
UniversityofNorthCarolinaatChapelHill,ChapelHill,NC,UnitedStates
LinShi
TheChineseUniversityofHongKong,HongKong,China
Hoo-ChangShin
NationalInstitutesofHealthClinicalCenter,Bethesda,MD,UnitedStates
JaeY.Shin
ArizonaStateUniversity,Scottsdale,AZ,UnitedStates
VikasSingh
UniversityofWisconsin–Madison,Madison,WI,UnitedStates
StefanSommer
UniversityofCopenhagen,Copenhagen,Denmark
SurajSrinivas
IndianInstituteofScience,Bangalore,India
Heung-IlSuk
KoreaUniversity,Seoul,RepublicofKorea
RonaldM.Summers
NationalInstitutesofHealthClinicalCenter,Bethesda,MD,UnitedStates
NimaTajbakhsh
ArizonaStateUniversity,Scottsdale,AZ,UnitedStates
Yuan-ChingTeng
UniversityofCopenhagen,Copenhagen,Denmark
RavitejaVemulapalli
UniversityofMaryland,CollegePark,MD,UnitedStates
DefengWang
TheChineseUniversityofHongKong,HongKong,China
JaneZ.Wang
UniversityofBritishColumbia,Vancouver,BC,Canada
ShaoyuWang
UniversityofNorthCarolinaatChapelHill,ChapelHill,NC,UnitedStates; DonghuaUniversity,Shanghai,China
LiorWolf
Tel-AvivUniversity,Ramat-Aviv,Israel
GuorongWu
UniversityofNorthCarolinaatChapelHill,ChapelHill,NC,UnitedStates
YuanpuXie
UniversityofFlorida,Gainesville,FL,UnitedStates
FuyongXing
UniversityofFlorida,Gainesville,FL,UnitedStates
ZhennanYan
RutgersUniversity,Piscataway,NJ,UnitedStates
LinYang
UniversityofFlorida,Gainesville,FL,UnitedStates
LequanYu
TheChineseUniversityofHongKong,HongKong,China
YiqiangZhan
SiemensHealthcare,Malvern,PA,UnitedStates
ShaotingZhang
UniversityofNorthCarolinaatCharlotte,Charlotte,NC,UnitedStates
LeiZhao
TheChineseUniversityofHongKong,HongKong,China
S.KevinZhou
SiemensHealthineersTechnologyCenter,Princeton,NJ,UnitedStates
XiangSeanZhou
SiemensHealthcare,Malvern,PA,UnitedStates
GaliZimmerman
Tel-AvivUniversity,Ramat-Aviv,Israel
AbouttheEditors
S.KevinZhou,PhD,iscurrentlyaPrincipalKeyExpertatSiemensHealthineers TechnologyCenter,leadingateamoffull-timeresearchscientistsandstudentsdedicatedtoresearchinganddevelopinginnovativesolutionsformedicalandindustrial imagingproducts.Hisresearchinterestslieincomputervisionandmachine/deep learningandtheirapplicationstomedicalimageanalysis,facerecognition,andmodeling,etc.Hehaspublishedover150bookchaptersandpeer-reviewedjournaland conferencepapers,registeredover250patentsandinventions,writtentworesearch monographs,andeditedthreebooks.Hehaswonmultipletechnology,patent,and productawards,includingtheR&D100AwardandSiemensInventoroftheYear.He isaneditorialboardmemberfortheMedicalImageAnalysisandIEEETransactions onMedicalImagingjournalsandafellowoftheAmericanInstituteofMedicaland BiologicalEngineering(AIMBE).
HayitGreenspan isaProfessorattheBiomedicalEngineeringDepartment,Faculty ofEngineering,TelAvivUniversity.ShewasavisitingProfessorattheRadiologyDepartmentofStanfordUniversity,andiscurrentlyaffiliatedwiththeInternationalComputerScienceInstitute(ICSI)atBerkeley.Dr.Greenspan’sresearch focusesonimagemodelingandanalysis,deeplearning,andcontent-basedimage retrieval.ResearchprojectsincludebrainMRIresearch(structuralandDTI),CT andX-rayimageanalysis–automateddetectiontosegmentationandcharacterization.Dr.Greenspanhasover150publicationsinleadinginternationaljournalsand conferenceproceedings.Shehasreceivedseveralawardsandisacoauthoronseveralpatents.Dr.Greenspanisamemberofseveraljournalandconferenceprogram committees,includingSPIEMedicalImaging,IEEE_ISBI,andMICCAI.Sheisan AssociateEditorfortheIEEETransactionsonMedicalImaging(TMI)journal.Recently,inMay2016,shewastheleadguesteditorforanIEEE-TMIspecialissueon “DeepLearninginMedicalImaging.”
DinggangShen isaProfessorofRadiologyattheBiomedicalResearchImaging Center(BRIC),andComputerScience,andBiomedicalEngineeringDepartmentsin theUniversityofNorthCarolinaatChapelHill(UNC-CH).Heiscurrentlydirecting theCenterforImageInformaticsandAnalysis,theImageDisplay,Enhancement,and Analysis(IDEA)LabintheDepartmentofRadiology,andalsothemedicalimage analysiscoreintheBRIC.Hewasatenure-trackassistantprofessorattheUniversityofPennsylvania(UPenn),andafacultymemberatJohnsHopkinsUniversity. Dr.Shen’sresearchinterestsincludemedicalimageanalysis,computervision,and patternrecognition.Hehaspublishedmorethan700papersininternationaljournals andconferenceproceedings.Heservesasaneditorialboardmemberforsixinternationaljournals.HehasservedontheBoardofDirectorsofTheMedicalImage ComputingandComputerAssistedIntervention(MICCAI)Societyin2012–2015.
Foreword
ComputationalMedicalImageAnalysishasbecomeaprominentfieldofresearch attheintersectionofInformatics,ComputationalSciences,andMedicine,supported byavibrantcommunityofresearchersworkinginacademics,industry,andclinical centers.
Duringthepastfewyears,MachineLearningmethodshavebroughtarevolution totheComputerVisioncommunity,introducingnovelefficientsolutionstomany imageanalysisproblemsthathadlongremainedunsolved.Forthisrevolutiontoenter thefieldofMedicalImageAnalysis,dedicatedmethodsmustbedesignedwhichtake intoaccountthespecificityofmedicalimages.
Indeed,medicalimagescapturetheanatomyandphysiologyofpatientsthrough themeasurementsofgeometrical,biophysical,andbiochemicalpropertiesoftheir livingtissues.Theseimagesareacquiredwithalgorithmsthatexploitcomplexmedicalimagingprocesseswhoseprinciplesmustbewellunderstoodaswellasthose governingthecomplexstructuresandfunctionsofthehumanbody.
Thebook DeepLearningforMedicalImageAnalysis editedbyS.KevinZhou, HayitGreenspan,andDinggangShen,top-notchresearchersfrombothacademiaand industryindesigningmachinelearningmethodsformedicalimageanalysis,coversstate-of-the-artreviewsofdeeplearningapproachesformedicalimageanalysis, includingmedicalimagedetection/recognition,medicalimagesegmentation,medicalimageregistration,computeraideddiagnosisanddiseasequantification,toname someofthemostimportantaddressedproblems.Thebook,whichstartswithanintroductiontoConvolutionalNeuralNetworksforComputerVisionpresentsasetof noveldeeplearningmethodsappliedtoavarietyofclinicalproblemsandimaging modalitiesoperatingatvariousscales,includingX-rayradiographies,MagneticResonanceImaging,ComputedTomography,microscopicimaging,ultrasoundimaging, etc.
Thisimpressivecollectionofexcellentcontributionswilldefinitelyserveand inspirealltheresearchersinterestedinthedevelopmentofnewmachinelearning methodsintherapidlyevolvingfieldofmedicalimageanalysis.
NicholasAyache,PhD
Inria,SophiaAntipolis,France September1,2016
Introduction
1 PART
