Deep Learning for Medical Image Analysis

459 Pages·2017·9.37 MB·English

by  S. Kevin Zhou

Deep Learning for
Medical Image Analysis
Edited by
S. Kevin Zhou
Hayit Greenspan
Dinggang Shen
Deep Learning for
Medical Image
Analysis
The Elsevier and MICCAI Society
Book Series
Advisory board
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
Deep Learning for
Medical Image
Analysis
Edited by
S. Kevin Zhou
Hayit Greenspan
Dinggang Shen
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Contents
Contributors........................................................ xv
AbouttheEditors ................................................... xxi
Foreword .......................................................... xxiii
PART 1 INTRODUCTION
CHAPTER 1 An Introduction to Neural Networks and Deep
Learning .......................................... 3
Heung-Il Suk
1.1 Introduction......................................... 3
1.2 Feed-ForwardNeuralNetworks ........................ 4
1.2.1 Perceptron .................................... 4
1.2.2 Multi-LayerNeuralNetwork..................... 5
1.2.3 LearninginFeed-ForwardNeuralNetworks ........ 6
1.3 ConvolutionalNeuralNetworks ........................ 8
1.3.1 ConvolutionandPoolingLayer................... 8
1.3.2 ComputingGradients ........................... 9
1.4 DeepModels........................................ 11
1.4.1 VanishingGradientProblem ..................... 11
1.4.2 DeepNeuralNetworks.......................... 12
1.4.3 DeepGenerativeModels ........................ 14
1.5 TricksforBetterLearning ............................. 20
1.5.1 RectifiedLinearUnit(ReLU) .................... 20
1.5.2 Dropout ...................................... 20
1.5.3 BatchNormalization............................ 21
1.6 Open-SourceToolsforDeepLearning................... 22
References.......................................... 22
Notes .............................................. 24
CHAPTER 2 An Introduction to Deep Convolutional Neural
Nets for Computer Vision ......................... 25
Suraj Srinivas, Ravi K. Sarvadevabhatla,
Konda R. Mopuri, Nikita Prabhu,
Srinivas S.S. Kruthiventi and R. VenkateshBabu
2.1 Introduction......................................... 26
2.2 ConvolutionalNeuralNetworks ........................ 27
2.2.1 BuildingBlocksofCNNs ....................... 27
2.2.2 Depth ........................................ 29
2.2.3 LearningAlgorithm ............................ 30
v
vi Contents
2.2.4 TrickstoIncreasePerformance................... 31
2.2.5 PuttingItAllTogether:AlexNet.................. 32
2.2.6 UsingPre-TrainedCNNs........................ 32
2.2.7 ImprovingAlexNet............................. 33
2.3 CNNFlavors ........................................ 34
2.3.1 Region-BasedCNNs............................ 34
2.3.2 FullyConvolutionalNetworks.................... 35
2.3.3 Multi-ModalNetworks.......................... 38
2.3.4 CNNswithRNNs .............................. 40
2.3.5 HybridLearningMethods ....................... 43
2.4 SoftwareforDeepLearning ........................... 45
References.......................................... 46
PART 2 MEDICAL IMAGE DETECTION AND RECOGNITION
CHAPTER 3 Efficient Medical Image Parsing .................. 55
Florin C. Ghesu, Bogdan Georgescu and
Joachim Hornegger
3.1 Introduction......................................... 55
3.2 BackgroundandMotivation ........................... 57
3.2.1 ObjectLocalizationandSegmentation:Challenges .. 58
3.3 Methodology........................................ 58
3.3.1 ProblemFormulation ........................... 58
3.3.2 SparseAdaptiveDeepNeuralNetworks ........... 59
3.3.3 MarginalSpaceDeepLearning................... 61
3.3.4 AnArtificialAgentforImageParsing ............. 64
3.4 Experiments......................................... 71
3.4.1 AnatomyDetectionandSegmentationin3D........ 71
3.4.2 LandmarkDetectionin2Dand3D................ 74
3.5 Conclusion.......................................... 77
Disclaimer .......................................... 78
References.......................................... 78
CHAPTER 4 Multi-Instance Multi-Stage Deep Learning for
Medical Image Recognition....................... 83
Zhennan Yan, Yiqiang Zhan, Shaoting Zhang,
Dimitris Metaxas and Xiang Sean Zhou
4.1 Introduction......................................... 83
4.2 RelatedWork........................................ 85
4.3 Methodology........................................ 87
4.3.1 ProblemStatementandFrameworkOverview....... 87
4.3.2 LearningStageI:Multi-InstanceCNNPre-Train .... 88
4.3.3 LearningStageII:CNNBoosting................. 90
Contents vii
4.3.4 Run-TimeClassification......................... 92
4.4 Results ............................................. 93
4.4.1 ImageClassificationonSyntheticData ............ 93
4.4.2 Body-PartRecognitiononCTSlices .............. 95
4.5 DiscussionandFutureWork ........................... 99
References.......................................... 100
CHAPTER 5 Automatic Interpretation of Carotid Intima–Media
Thickness Videos Using Convolutional Neural
Networks.......................................... 105
Nima Tajbakhsh,Jae Y. Shin, R. Todd Hurst,
Christopher B. Kendall and Jianming Liang
5.1 Introduction......................................... 106
5.2 RelatedWork........................................ 107
5.3 CIMTProtocol ...................................... 109
5.4 Method............................................. 109
5.4.1 ConvolutionalNeuralNetworks(CNNs) ........... 109
5.4.2 FrameSelection................................ 110
5.4.3 ROILocalization............................... 112
5.4.4 Intima–MediaThicknessMeasurement ............ 115
5.5 Experiments......................................... 117
5.5.1 Pre-andPost-ProcessingforFrameSelection....... 118
5.5.2 ConstrainedROILocalization.................... 118
5.5.3 Intima–MediaThicknessMeasurement ............ 121
5.5.4 End-to-EndCIMTMeasurement.................. 123
5.6 Discussion .......................................... 124
5.7 Conclusion.......................................... 128
Acknowledgement ................................... 128
References.......................................... 128
Notes .............................................. 131
CHAPTER 6 Deep Cascaded Networks for Sparsely Distributed
Object Detection from Medical Images ........... 133
Hao Chen, Qi Dou, Lequan Yu, Jing Qin, Lei Zhao,
Vincent C.T. Mok, Defeng Wang, Lin Shi and
Pheng-Ann Heng
6.1 Introduction......................................... 134
6.2 Method............................................. 136
6.2.1 CoarseRetrievalModel ......................... 136
6.2.2 FineDiscriminationModel ...................... 139
6.3 MitosisDetectionfromHistologyImages ................ 139
6.3.1 Background ................................... 139
6.3.2 TransferLearningfromCross-Domain............. 140
viii Contents
6.3.3 DatasetandPreprocessing ....................... 140
6.3.4 QuantitativeEvaluationandComparison........... 141
6.3.5 ComputationCost.............................. 142
6.4 CerebralMicrobleedDetectionfromMRVolumes......... 143
6.4.1 Background ................................... 143
6.4.2 3DCascadedNetworks ......................... 144
6.4.3 DatasetandPreprocessing ....................... 146
6.4.4 QuantitativeEvaluationandComparison........... 147
6.4.5 SystemImplementation ......................... 149
6.5 DiscussionandConclusion ............................ 149
Acknowledgements .................................. 150
References.......................................... 150
Notes .............................................. 154
CHAPTER 7 Deep Voting and Structured Regression for
Microscopy Image Analysis....................... 155
YuanpuXie, Fuyong Xing and Lin Yang
7.1 Deep Voting: A Robust Approach Toward Nucleus
LocalizationinMicroscopyImages ..................... 156
7.1.1 Introduction................................... 156
7.1.2 Methodology .................................. 158
7.1.3 WeightedVotingDensityEstimation .............. 162
7.1.4 Experiments................................... 163
7.1.5 Conclusion.................................... 165
7.2 Structured Regression for Robust Cell Detection Using
ConvolutionalNeuralNetwork ......................... 165
7.2.1 Introduction................................... 165
7.2.2 Methodology .................................. 166
7.2.3 ExperimentalResults ........................... 169
7.2.4 Conclusion.................................... 171
Acknowledgements .................................. 172
References.......................................... 172
PART 3 MEDICAL IMAGE SEGMENTATION
CHAPTER 8 Deep Learning Tissue Segmentation in Cardiac
Histopathology Images............................ 179
JeffreyJ.Nirschl,AndrewJanowczyk,EliotG.Peyster,
Renee Frank, Kenneth B. Margulies,
Michael D. Feldman and Anant Madabhushi
8.1 Introduction......................................... 180
8.2 ExperimentalDesignandImplementation................ 183
Contents ix
8.2.1 DataSetDescription............................ 183
8.2.2 ManualGroundTruthAnnotations................ 183
8.2.3 Implementation................................ 183
8.2.4 TrainingaModelUsingEngineeredFeatures ....... 185
8.2.5 Experiments................................... 186
8.2.6 TestingandPerformanceEvaluation............... 188
8.3 ResultsandDiscussion................................ 188
8.3.1 Experiment1:ComparisonofDeepLearningand
RandomForestSegmentation .................... 188
8.3.2 Experiment2:EvaluatingtheSensitivityofDeep
LearningtoTrainingData ....................... 188
8.4 ConcludingRemarks ................................. 191
Notes .............................................. 191
DisclosureStatement ................................. 191
Funding ............................................ 192
References.......................................... 192
CHAPTER 9 Deformable MR Prostate Segmentation via Deep
Feature Learning and Sparse Patch Matching .... 197
Yanrong Guo, Yaozong Gao and Dinggang Shen
9.1 Background......................................... 197
9.2 ProposedMethod .................................... 199
9.2.1 RelatedWork.................................. 199
9.2.2 LearningDeepFeatureRepresentation............. 201
9.2.3 SegmentationUsingLearnedFeatureRepresentation. 206
9.3 Experiments......................................... 211
9.3.1 Evaluation of the Performance of Deep-Learned
Features ...................................... 212
9.3.2 EvaluationofthePerformanceofDeformableModel 216
9.4 Conclusion.......................................... 219
References.......................................... 219
CHAPTER 10 Characterizationof Errors inDeep Learning-Based
Brain MRI Segmentation .......................... 223
Akshay Pai, Yuan-Ching Teng, Joseph Blair,
Michiel Kallenberg, Erik B. Dam, Stefan Sommer,
Christian Igel and Mads Nielsen
10.1 Introduction......................................... 224
10.2 DeepLearningforSegmentation ....................... 225
10.3 ConvolutionalNeuralNetworkArchitecture .............. 226
10.3.1BasicCNNArchitecture......................... 226
10.3.2Tri-PlanarCNNfor3DImageAnalysis............ 227