Artificial Intelligence in Digital Holographic Imaging

339 Pages·2023·43.434 MB·English

by  Inkyu Moon

Artificial Intelligence in Digital HolographicImaging

Artificial Intelligence in Digital
Holographic Imaging
Technical Basis and Biomedical Applications
Inkyu Moon
Department of Robotics and Mechatronics Engineering
Daegu Gyeongbuk Institute of Science & Technology (DGIST)
Daegu, South Korea
Thiseditionfirstpublished2023
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v
Contents
Preface ix
PartI DigitalHolographicImaging 1
1 Introduction 3
References 6
2 CoherentOpticalImaging 7
2.1 MonochromaticFieldsandIrradiance 7
2.2 AnalyticExpressionforFresnelDiffraction 8
2.3 LensTransmittanceFunction 10
2.4 GeometricalImagingConcepts 11
2.5 CoherentImagingTheory 13
References 15
3 LateralandDepthResolutions 17
3.1 LateralResolution 17
3.2 Depth(orAxial)Resolution 21
References 23
4 PhaseUnwrapping 25
4.1 BranchCuts 27
4.2 Quality-guided,Path-followingAlgorithms 29
References 32
5 Off-axisDigitalHolographicMicroscopy 35
5.1 Off-axisDigitalHolographicMicroscopyDesigns 35
5.2 DigitalHologramReconstruction 37
References 40
6 GaborDigitalHolographicMicroscopy 43
6.1 Introduction 43
6.2 Methodology 43
References 46
vi Contents
PartII DeepLearninginDigitalHolographicMicroscopy(DHM) 47
7 Introduction 49
References 50
8 No-searchFocusPredictioninDHMwithDeepLearning 53
8.1 Introduction 53
8.2 MaterialsandMethods 56
8.3 ExperimentalResults 60
8.4 Conclusions 65
References 66
9 AutomatedPhaseUnwrappinginDHMwithDeepLearning 69
9.1 Introduction 69
9.2 Deep-learningModel 71
9.3 UnwrappingwithDeep-learningModel 75
9.4 Conclusions 84
References 84
10 Noise-freePhaseImaginginGaborDHMwithDeepLearning 87
10.1 Introduction 87
10.2 ADeep-learningModelforGaborDHM 88
10.3 ExperimentalResults 94
10.4 Discussion 100
10.5 Conclusions 103
References 103
PartIII IntelligentDigitalHolographicMicroscopy(DHM)for
BiomedicalApplications 107
11 Introduction 109
References 110
12 RedBloodCellPhase-imageSegmentation 113
12.1 Introduction 113
12.2 Marker-controlledWatershedAlgorithm 114
12.3 SegmentationBasedonMarker-controlledWatershedAlgorithm 116
12.4 ExperimentalResults 118
12.5 PerformanceEvaluation 120
12.6 Conclusions 123
References 124
13 RedBloodCellPhase-imageSegmentationwithDeepLearning 127
13.1 Introduction 127
13.2 FullyConvolutionalNeuralNetworks 128
13.3 RBCPhase-imageSegmentationviaDeepLearning 130
Contents vii
13.4 ExperimentalResults 132
13.5 Conclusions 136
References 137
14 AutomatedPhenotypicClassificationofRedBloodCells 139
14.1 Introduction 139
14.2 FeatureExtraction 141
14.3 PatternRecognitionNeuralNetwork 144
14.4 ExperimentalResultsandDiscussion 146
14.5 Conclusions 153
References 153
15 AutomatedAnalysisofRedBloodCellStorageLesions 155
15.1 Introduction 155
15.2 QuantitativeAnalysisofRBC3DMorphologicalChanges 156
15.3 ExperimentalResultsandDiscussion 159
15.4 Conclusions 170
References 170
16 AutomatedRedBloodCellClassificationwithDeepLearning 173
16.1 Introduction 173
16.2 ProposedDeep-learningModel 177
16.3 ExperimentalResults 181
16.4 Conclusions 187
References 188
17 High-throughputLabel-freeCellCountingwithDeepNeural
Networks 191
17.1 Introduction 191
17.2 MaterialsandMethods 192
17.3 ExperimentalResults 198
17.4 Conclusions 207
References 208
18 AutomatedTrackingofTemporalDisplacementsofRedBloodCells 209
18.1 Introduction 209
18.2 Mean-shiftTrackingAlgorithm 211
18.3 KalmanFilter 213
18.4 ProcedureforSingleRBCTracking 215
18.5 ExperimentalResults 219
18.6 Conclusions 224
References 224
19 AutomatedQuantitativeAnalysisofRedBloodCellDynamics 227
19.1 Introduction 227
19.2 RBCParameters 228
viii Contents
19.3 QuantitativeAnalysisofRBCFluctuations 233
19.4 Conclusions 238
References 238
20 QuantitativeAnalysisofRedBloodCellsduringTemperature
Elevation 241
20.1 Introduction 241
20.2 RBCSamplePreparations 241
20.3 ExperimentalResults 242
20.4 Conclusions 247
References 247
21 AutomatedMeasurementofCardiomyocyteDynamicswithDHM 249
21.1 Introduction 249
21.2 CellCultureandImaging 250
21.3 AutomatedAnalysisofCardiomyocyteDynamics 250
21.4 Conclusions 263
References 264
22 AutomatedAnalysisofCardiomyocyteswithDeepLearning 267
22.1 Introduction 267
22.2 Region-of-interestIdentificationwithDynamicBeatingActivity
Analysis 268
22.3 DeepNeuralNetworkforCardiomyocyteImageSegmentation 268
22.4 ExperimentalResults 272
22.5 Conclusions 284
References 285
23 AutomaticQuantificationofDrug-treatedCardiomyocyteswithDHM 287
23.1 Introduction 287
23.2 MaterialsandMethods 288
23.3 ExperimentalResultsandDiscussion 295
23.4 Conclusions 300
References 301
24 AnalysisofCardiomyocyteswithHolographicImage-basedTracking 303
24.1 Introduction 303
24.2 MaterialsandMethods 304
24.3 ExperimentalResultsandDiscussion 307
24.4 Conclusions 313
References 314
25 ConclusionandFutureWork 315
Index 319