Table Of ContentDeep Network Design
for Medical Image
Computing
Principles and Applications
THE ELSEVIER AND MICCAI SOCIETY
BOOK SERIES
Advisory Board
NicholasAyache
JamesS.Duncan
AlexFrangi
HayitGreenspan
PierreJannin
AnneMartel
XavierPennec
TerryPeters
DanielRueckert
MilanSonka
JayTian
S.KevinZhou
Titles
Balocco,A.,etal.,ComputingandVisualizationforIntravascularImagingand
ComputerAssistedStenting,9780128110188.
Dalca,A.V.,etal.,ImagingGenetics,9780128139684.
Depeursinge,A.,etal.,BiomedicalTextureAnalysis,9780128121337.
Munsell,B.,etal.,Connectomics,9780128138380.
Pennec,X.,etal.,RiemannianGeometricStatisticsinMedical
ImageAnalysis,9780128147252.
Trucco,E.,etal.,ComputationalRetinalImageAnalysis,9780081028162.
Wu,G.,andSabuncu,M.,MachineLearningandMedicalImaging,9780128040768.
ZhouS.K.,MedicalImageRecognition,SegmentationandParsing,9780128025819.
Zhou,S.K.,etal.,DeepLearningforMedicalImageAnalysis,9780128104088.
Zhou,S.K.,etal.,HandbookofMedicalImageComputingandComputer
AssistedIntervention,9780128161760.
Deep Network Design
for Medical Image
Computing
Principles and Applications
Haofu Liao
S. Kevin Zhou
Jiebo Luo
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Contents
Listoffigures ................................................. xi
Acknowledgments ............................................. xvii
CHAPTER 1 Introduction .................................... 1
1.1 Medicalimagecomputing ........................... 1
1.1.1 Medicalimagereconstruction ................... 2
1.1.2 Medicalimageanalysis ........................ 2
1.1.3 Medicalimagecomputingasfunctionalapproximation 3
1.2 Deeplearningdesignprinciples ....................... 4
1.2.1 Computervisiontechniquesformedicalimage
computing.................................. 4
1.2.2 Machinelearningtechniquesformedicalimage
computing.................................. 5
1.2.3 Medicaldomainknowledge..................... 5
1.3 Chapterorganization ............................... 6
References....................................... 8
CHAPTER 2 Deep learning basics ........................... 11
2.1 Convolutionalneuralnetworks ........................ 11
2.1.1 3Dconvolutionalneuralnetworks ................ 12
2.2 Recurrentneuralnetworks ........................... 13
2.2.1 Longshort-termmemory....................... 14
2.2.2 BidirectionalRNN ........................... 15
2.3 Deepimage-to-imagenetworks ....................... 16
2.3.1 Retainingspatialresolutions .................... 16
2.3.2 Fullyconvolutionalnetworks.................... 17
2.3.3 Encoder–decodernetworks ..................... 17
2.4 Deepgenerativenetworks ........................... 18
2.4.1 Basicmodels................................ 19
References....................................... 21
PART 1 Deep network design for medical image
analysis and selected applications
CHAPTER 3 Classification: lesion and disease recognition .... 27
3.1 Designprinciples .................................. 28
3.1.1 Choiceofdeepneuralnetworks .................. 28
3.1.2 Choiceofclassificationtasksandobjectives ........ 29
3.1.3 Transferlearning ............................. 31
3.1.4 Multitasklearning ............................ 32
vii
viii Contents
3.2 Casestudy:skindiseaseclassificationversusskinlesion
characterization ................................... 33
3.2.1 Background ................................ 35
3.2.2 Dataset .................................... 35
3.2.3 Methodology ............................... 37
3.2.4 Experiments ................................ 38
3.2.5 Discussion ................................. 42
3.3 Casestudy:skinlesionclassificationwithmultitasklearning . 44
3.3.1 Background ................................ 44
3.3.2 Dataset .................................... 45
3.3.3 Methodology ............................... 47
3.3.4 Experiments ................................ 49
3.3.5 Discussion ................................. 52
3.4 Summary ........................................ 53
References....................................... 54
CHAPTER 4 Detection: vertebrae localization and identification 59
4.1 Designprinciples .................................. 60
4.1.1 Choiceofdeepneuralnetworks .................. 60
4.1.2 Choiceofdetectiontasksandobjectives ........... 64
4.2 Casestudy:vertebraelocalizationandidentification ........ 67
4.2.1 Background ................................ 69
4.2.2 Methodology ............................... 70
4.2.3 Experiments ................................ 76
4.2.4 Discussion ................................. 80
4.3 Summary ........................................ 81
References....................................... 83
CHAPTER 5 Segmentation: intracardiac echocardiography
contouring ..................................... 89
5.1 Designprinciples .................................. 90
5.1.1 Choiceofdeepneuralnetworks .................. 90
5.1.2 Choiceofsegmentationtasksandobjectives ........ 92
5.1.3 Imagerestorationforsegmentation ............... 94
5.2 Casestudy:intracardiacechocardiographycontouring ...... 96
5.2.1 Methodology ............................... 97
5.2.2 Experiments ................................ 99
5.2.3 Discussion ................................. 102
5.3 Summary ........................................ 102
References....................................... 103
CHAPTER 6 Registration: 2D/3D rigid registration............. 109
6.1 Designprinciples .................................. 111
6.1.1 Deepsimilaritybasedregistration ................ 111
6.1.2 Reinforcementlearningbasedregistration .......... 112
Contents ix
6.1.3 Supervisedtransformationestimation ............. 113
6.1.4 Unsupervisedtransformationestimation ........... 115
6.2 Casestudy:2D/3Dmedicalimageregistration ............ 117
6.2.1 Problemformulation .......................... 119
6.2.2 Methodology ............................... 121
6.2.3 Experiments ................................ 125
6.2.4 Limitations ................................. 130
6.2.5 Discussion ................................. 130
6.3 Summary ........................................ 130
References....................................... 131
PART 2 Deep network design for medical image
reconstruction, synthesis, and selected applications
CHAPTER 7 Reconstruction: supervised artifact reduction ..... 137
7.1 Designprinciples .................................. 138
7.1.1 Imagedomainapproaches ...................... 138
7.1.2 Sensordomainapproaches ..................... 139
7.1.3 Dual-domainapproaches ....................... 141
7.2 Casestudy:sparse-viewartifactreduction ............... 142
7.2.1 Background ................................ 143
7.2.2 Methodology ............................... 144
7.2.3 Experiments ................................ 147
7.2.4 Discussion ................................. 149
7.3 Casestudy:metalartifactreduction .................... 150
7.3.1 Background ................................ 152
7.3.2 Methodology ............................... 154
7.3.3 Experiments ................................ 156
7.3.4 Discussion ................................. 162
7.4 Summary ........................................ 162
References....................................... 163
CHAPTER 8 Reconstruction: unsupervised artifact reduction... 169
8.1 Designprinciples .................................. 170
8.1.1 Unpairedlearningapproaches ................... 170
8.1.2 Self-supervisedlearningapproaches .............. 172
8.2 Casestudy:metalartifactreduction .................... 175
8.2.1 Background ................................ 177
8.2.2 Methodology ............................... 177
8.2.3 Experiments ................................ 183
8.2.4 Discussion ................................. 191
8.3 Summary ........................................ 193
References....................................... 194
