Handbook of medical image computing and computer assisted intervention

1054 Pages·2020·17.523 MB·English

by  Zhou S.K (ed.)

HANDBOOK OF
MEDICAL IMAGE
COMPUTING AND
COMPUTER
ASSISTED
INTERVENTION
THE ELSEVIER AND MICCAI SOCIETY
BOOK SERIES
Advisory Board
Nicholas Ayache
James S. Duncan
Alex Frangi
Hayit Greenspan
Pierre Jannin
Anne Martel
Xavier Pennec
Terry Peters
Daniel Rueckert
Milan Sonka
Jay Tian
S. Kevin Zhou
Titles:
Balocco, A., et al.,Computing and Visualizationfor IntravascularImaging and
Computer AssistedStenting, 9780128110188
Dalca, A.V.,et al., ImagingGenetics, 9780128139684
Depeursinge, A., et al.,Biomedical Texture Analysis,9780128121337
Munsell,B., et al.,Connectomics, 9780128138380
Pennec, X., et al., Riemannian Geometric Statistics in Medical Image Analysis,
9780128147252
Wu, G., andSabuncu, M., Machine Learning andMedical Imaging,9780128040768
Zhou S.K.,Medical Image Recognition, Segmentation and Parsing,9780128025819
Zhou, S.K., et al.,Deep Learningfor Medical Image Analysis,9780128104088
Zhou, S.K., et al.,Handbook of Medical Image Computing and Computer Assisted
Intervention,9780128161760
HANDBOOK OF
MEDICAL IMAGE
COMPUTING AND
COMPUTER
ASSISTED
INTERVENTION
Editedby
S.KEVINZHOU
InstituteofComputingTechnology
ChineseAcademyofSciences
Beijing,China
DANIELRUECKERT
ImperialCollegeLondon
London,UnitedKingdom
GABORFICHTINGER
Queen’sUniversity
Kingston,ON,Canada
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Contents
Contributors xvii
Acknowledgment xxvii
1. Imagesynthesisandsuperresolutioninmedicalimaging 1
JerryL.Prince,AaronCarass,CanZhao,BlakeE.Dewey,SnehashisRoy,DzungL.Pham
1.1. Introduction 1
1.2. Imagesynthesis 2
1.3. Superresolution 12
1.4. Conclusion 18
References 19
2. Machinelearningforimagereconstruction 25
KerstinHammernik,FlorianKnoll
2.1. Inverseproblemsinimaging 26
2.2. Unsupervisedlearninginimagereconstruction 30
2.3. Supervisedlearninginimagereconstruction 32
2.4. Trainingdata 48
2.5. Lossfunctionsandevaluationofimagequality 49
2.6. Discussion 53
Acknowledgments 54
References 54
3. LiverlesiondetectioninCTusingdeeplearningtechniques 65
AviBen-Cohen,HayitGreenspan
3.1. Introduction 65
3.2. FullyconvolutionalnetworkforliverlesiondetectioninCTexaminations 68
3.3. FullyconvolutionalnetworkforCTtoPETsynthesistoaugmentmalignantliverlesion
detection 77
3.4. Discussionandconclusions 84
Acknowledgments 87
References 87
4. CADinlung 91
KensakuMori
4.1. Overview 91
4.2. OriginoflungCAD 92
4.3. LungCADsystems 92
4.4. Localizeddisease 93
v
vi Contents
4.5. Diffuselungdisease 96
4.6. Anatomicalstructureextraction 98
References 104
5. Textmininganddeeplearningfordiseaseclassification 109
YifanPeng,ZizhaoZhang,XiaosongWang,LinYang,LeLu
5.1. Introduction 109
5.2. Literaturereview 110
5.3. Casestudy1:textmininginradiologyreportsandimages 114
5.4. Casestudy2:textmininginpathologyreportsandimages 122
5.5. Conclusionandfuturework 129
Acknowledgments 130
References 130
6. Multiatlassegmentation 137
BennettA.Landman,IlwooLyu,YuankaiHuo,AndrewJ.Asman
6.1. Introduction 138
6.2. Historyofatlas-basedsegmentation 139
6.3. Mathematicalframework 146
6.4. Connectionbetweenmultiatlassegmentationandmachinelearning 154
6.5. Multiatlassegmentationusingmachinelearning 154
6.6. Machinelearningusingmultiatlassegmentation 155
6.7. Integratingmultiatlassegmentationandmachinelearning 155
6.8. Challengesandapplications 155
6.9. Unsolvedproblems 156
Glossary 157
References 158
7. Segmentationusingadversarialimage-to-imagenetworks 165
DongYang,TaoXiong,DaguangXu,S.KevinZhou
7.1. Introduction 165
7.2. Segmentationusinganadversarialimage-to-imagenetwork 169
7.3. Volumetricdomainadaptationwithintrinsicsemanticcycleconsistency 172
References 181
8. Multimodalmedicalvolumestranslationandsegmentationwithgenerative
adversarialnetwork 183
ZizhaoZhang,LinYang,YefengZheng
8.1. Introduction 183
8.2. Literaturereview 186
8.3. Preliminary 188
8.4. Method 190
Contents vii
8.5. Networkarchitectureandtrainingdetails 192
8.6. Experimentalresults 194
8.7. Conclusions 200
References 201
9. Landmarkdetectionandmultiorgansegmentation:Representationsand
supervisedapproaches 205
S.KevinZhou,ZhoubingXu
9.1. Introduction 205
9.2. Landmarkdetection 207
9.3. Multiorgansegmentation 217
9.4. Conclusion 227
References 227
10.Deepmultilevelcontextualnetworksforbiomedicalimagesegmentation 231
HaoChen,QiDou,XiaojuanQi,Jie-ZhiCheng,Pheng-AnnHeng
10.1. Introduction 231
10.2. Relatedwork 233
10.3. Method 235
10.4. Experimentsandresults 237
10.5. Discussionandconclusion 244
Acknowledgment 244
References 245
11.LOGISMOS-JEI:Segmentationusingoptimalgraphsearchandjust-enough
interaction 249
HonghaiZhang,KyungmooLee,ZhiChen,SatyanandaKashyap,MilanSonka
11.1. Introduction 249
11.2. LOGISMOS 251
11.3. Just-enoughinteraction 256
11.4. RetinalOCTsegmentation 257
11.5. CoronaryOCTsegmentation 260
11.6. KneeMRsegmentation 265
11.7. Modularapplicationdesign 268
11.8. Conclusion 269
Acknowledgments 270
References 270
12.Deformablemodels,sparsityandlearning-basedsegmentationforcardiacMRI
basedanalytics 273
DimitrisN.Metaxas,ZhennanYan
12.1. Introduction 273
viii Contents
12.2. Deeplearningbasedsegmentationofventricles 277
12.3. Shaperefinementbysparseshapecomposition 283
12.4. 3Dmodeling 285
12.5. Conclusionandfuturedirections 288
References 288
13.Imageregistrationwithslidingmotion 293
MattiasP.Heinrich,BartłomiejW.Papiez˙
13.1. Challengesofmotiondiscontinuitiesinmedicalimaging 293
13.2. SlidingpreservingregularizationforDemons 296
13.3. Discreteoptimizationfordisplacements 303
13.4. Imageregistrationforcancerapplications 311
13.5. Conclusions 313
References 314
14.Imageregistrationusingmachineanddeeplearning 319
XiaohuanCao,JingfanFan,PeiDong,SaharAhmad,Pew-ThianYap,DinggangShen
14.1. Introduction 319
14.2. Machine-learning-basedregistration 322
14.3. Machine-learning-basedmultimodalregistration 326
14.4. Deep-learning-basedregistration 332
References 338
15.ImagingbiomarkersinAlzheimer’sdisease 343
CaroleH.Sudre,M.JorgeCardoso,MarcModat,SebastienOurselin
15.1. Introduction 344
15.2. Rangeofimagingmodalitiesandassociatedbiomarkers 345
15.3. Biomarkerextractionevolution 349
15.4. Biomarkersinpractice 353
15.5. Biomarkers’strategies:practicalexamples 356
15.6. FutureavenuesofimageanalysisforbiomarkersinAlzheimer’sdisease 360
References 363
16.Machinelearningbasedimagingbiomarkersinlargescalepopulationstudies:
Aneuroimagingperspective 379
GurayErus,MohamadHabes,ChristosDavatzikos
16.1. Introduction 379
16.2. Largescalepopulationstudiesinneuroimageanalysis:stepstowardsdimensional
neuroimaging;harmonizationchallenges 382
16.3. Unsupervisedpatternlearningfordimensionalityreductionofneuroimagingdata 385
16.4. Supervisedclassificationbasedimagingbiomarkersfordiseasediagnosis 387
16.5. Multivariatepatternregressionforbrainageprediction 389
Contents ix
16.6. Deeplearninginneuroimaginganalysis 392
16.7. Revealingheterogeneityofimagingpatternsofbraindiseases 393
16.8. Conclusions 394
References 395
17.Imagingbiomarkersforcardiovasculardiseases 401
AvanSuinesiaputra,KathleenGilbert,BeauPontre,AlistairA.Young
17.1. Introduction 401
17.2. Cardiacimaging 402
17.3. Cardiacshapeandfunction 404
17.4. Cardiacmotion 409
17.5. Coronaryandvascularfunction 412
17.6. Myocardialstructure 416
17.7. Population-basedcardiacimagebiomarkers 419
References 420
18.Radiomics 429
MartijnP.A.Starmans,SebastianR.vanderVoort,JoseM.CastilloTovar,JifkeF.Veenland,
StefanKlein,WiroJ.Niessen
18.1. Introduction 430
18.2. Dataacquisition&preparation 431
18.3. Segmentation 434
18.4. Features 437
18.5. Datamining 441
18.6. Studydesign 444
18.7. Infrastructure 447
18.8. Conclusion 451
Acknowledgment 452
References 452
19.Randomforestsinmedicalimagecomputing 457
EnderKonukoglu,BenGlocker
19.1. Adifferentwaytousecontext 457
19.2. Featureselectionandensembling 459
19.3. Algorithmbasics 460
19.4. Applications 467
19.5. Conclusions 476
References 476
20.Convolutionalneuralnetworks 481
JonasTeuwen,NikitaMoriakov
20.1. Introduction 481