Machine Learning and Medical Imaging

488 Pages·2016·42.155 MB·English

by  Guorong Wu, Dinggang Shen, Mert Sabuncu

Machine Learning and
Medical Imaging
The Elsevier and MICCAI Society
Book Series
Advisory board
StephenAylward(Kitware,USA)
DavidHawkes(UniversityCollegeLondon,UnitedKingdom)
KensakuMori(UniversityofNagoya,Japan)
AlisonNoble(UniversityofOxford,UnitedKingdom)
SoniaPujol(HarvardUniversity,USA)
DanielRueckert(ImperialCollege,UnitedKingdom)
XavierPennec(INRIASophia-Antipolis,France)
PierreJannin(UniversityofRennes,France)
Alsoavailable:
Zhou,MedicalImageRecognition,SegmentationandParsing,
9780128025819
Machine Learning and
Medical Imaging
Guorong Wu
Dinggang Shen
Mert R. Sabuncu
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Contributors
K.Bhatia
ImperialCollegeLondon,London,UnitedKingdom
L.Bonilha
MedicalUniversityofSouthCarolina,Charleston,SC,UnitedStates
T.Brosch
TheUniversityofBritishColumbia,Vancouver,BC,Canada
J.Caballero
ImperialCollegeLondon,London,UnitedKingdom
C.Davatzikos
UniversityofPennsylvania,Philadelphia,PA,UnitedStates
M.DeCraene
PhilipsMedisys,Suresnes,France
M.Dewan
Flipkart,PaloAlto,CA,UnitedStates
A.Dong
UniversityofPennsylvania,Philadelphia,PA,UnitedStates
L.Du
IndianaUniversitySchoolofMedicine,Indianapolis,IN,UnitedStates
N.Duchateau
InriaSophiaAntipolis,SophiaAntipolis,France
H.Eavani
UniversityofPennsylvania,Philadelphia,PA,UnitedStates
A.Frangi
UniversityofSheffield,Sheffield,UnitedKingdom
J.Fridriksson
UniversityofSouthCarolina,Columbia,SC,UnitedStates
Y.Gao
SchoolofSoftware,TsinghuaUniversity,Beijing,China
B.Gaonkar
UniversityofPennsylvania,Philadelphia,PA,UnitedStates
T.Ge
Massachusetts General Hospital/Harvard Medical School, Boston; Massachu-
settsGeneralHospital,Boston;BroadInstituteofMITandHarvard,Cambridge,
MA,UnitedStates
T.He
HoustonMethodistResearchInstitute,Houston,TX,UnitedStates
xv
xvi Contributors
J.Huang
UniversityofTexasatArlington,Arlington,TX,UnitedStates
N.Honnorat
UniversityofPennsylvania,Philadelphia,PA,UnitedStates
B.Jian
Google,MountainView,CA,UnitedStates
X.Jiang
TheUniversityofGeorgia,Athens,GA,UnitedStates
S.Keller
UniversityofLiverpool,Liverpool,UnitedKingdom
R.Kong
NationalUniversityofSingapore,Singapore
Y.Li
UniversityofTexasatArlington,Arlington,TX,UnitedStates
M.Liu
UniversityofNorthCarolinaatChapelHill,ChapelHill,NC,UnitedStates;Nanjing
University of Aeronautics and Astronautics, Nanjing; Taishan University, Taian,
China
T.Liu
TheUniversityofGeorgia,Athens,GA,UnitedStates
R.Min
UniversityofNorthCarolinaatChapelHill,ChapelHill,NC,UnitedStates
B.C.Munsell
CollegeofCharleston,Charleston,SC,UnitedStates
Z.Peng
SiemensHealthcare,Malvern,PA,UnitedStates
G.Piella
UniversitatPompeuFabra,Barcelona,Spain
D.Rueckert
ImperialCollegeLondon,London,UnitedKingdom
M.R.Sabuncu
Massachusetts General Hospital/Harvard Medical School, Boston;
MassachusettsInstituteofTechnology,Cambridge,MA,UnitedStates
A.Schaefer
NationalUniversityofSingapore,Singapore
D.Shen
UniversityofNorthCarolinaatChapelHill,ChapelHill,NC,UnitedStates
L.Shen
IndianaUniversitySchoolofMedicine,Indianapolis,IN,UnitedStates
Contributors xvii
J.W.Smoller
MassachusettsGeneralHospital/HarvardMedicalSchool,Boston;BroadInstitute
ofMITandHarvard,Cambridge,MA,UnitedStates
A.Sotiras
UniversityofPennsylvania,Philadelphia,PA,UnitedStates
M.Stoner
UniversityofNorthCarolinaatChapelHill,ChapelHill,NC,UnitedStates
R.Tam
TheUniversityofBritishColumbia,Vancouver,BC,Canada
L.Y.W.Tang
TheUniversityofBritishColumbia,Vancouver,BC,Canada
B.T.ThomasYeo
NationalUniversityofSingapore,Singapore
P.Thompson
UniversityofSouthernCalifornia,LosAngeles,CA,UnitedStates
T.Tong
ImperialCollegeLondon,London,UnitedKingdom
E.Varol
UniversityofPennsylvania,Philadelphia,PA,UnitedStates
J.Wang
UniversityofMichigan,AnnArbor,MI,UnitedStates
B.Weber
UniversityofBonn,Bonn,Germany
G.Wu
UniversityofNorthCarolinaatChapelHill,ChapelHill,NC,UnitedStates
F.Xing
UniversityofFlorida,Gainesville,FL,UnitedStates
Z.Xue
HoustonMethodistResearchInstitute,Houston,TX,UnitedStates
J.Yan
IndianaUniversitySchoolofMedicine,Indianapolis,IN,UnitedStates
L.Yang
UniversityofFlorida,Gainesville,FL,UnitedStates
T.Yang
ArizonaStateUniversity,Tempe,AZ,UnitedStates
X.Yao
IndianaUniversitySchoolofMedicine,Indianapolis,IN,UnitedStates
xviii Contributors
J.Ye
UniversityofMichigan,AnnArbor,MI,UnitedStates
Y.Yoo
TheUniversityofBritishColumbia,Vancouver,BC,Canada
Y.Zhan
SiemensHealthcare,Malvern,PA,UnitedStates
D.Zhang
NanjingUniversityofAeronauticsandAstronautics,Nanjing,China
S.Zhang
UniversityofNorthCarolinaatCharlotte,Charlotte,NC,UnitedStates
X.Zhang
UniversityofNorthCarolinaatCharlotte,Charlotte,NC,UnitedStates
X.S.Zhou
SiemensHealthcare,Malvern,PA,UnitedStates
D.Zhu
UniversityofSouthernCalifornia,LosAngeles,CA,UnitedStates
Editor Biographies
Guorong Wu is an Assistant Professor of Radiology at the Biomedical Research
Imaging Center (BRIC) at the University of North Carolina at Chapel Hill. Dr. Wu
receivedhisPhDdegreefromtheDepartmentofComputerScienceatShanghaiJiao
Tong University in 2007. After graduation, he worked for Pixelworks and joined
the University of North Carolina at Chapel Hill in 2009. Dr. Wu’s research aims to
developcomputationaltoolsforbiomedicalimaginganalysisandcomputer-assisted
diagnosis. He is interested in medical image processing, machine learning, and
patternrecognition.Hehaspublishedmorethan100papersforinternationaljournals
and conferences. Dr. Wu is active in the development of medical image processing
softwarethatfacilitatesscientificresearchonneuroscienceandradiologytherapy.
Dinggang Shen is a Professor of Radiology at the Biomedical Research Imaging
Center (BRIC), and of Computer Science and Biomedical Engineering at the
University of North Carolina at Chapel Hill (UNC-CH). He is currently directing
the Center for Image Informatics and Analysis, the Image Display, Enhancement,
and Analysis (IDEA) Lab in the Department of Radiology, and the medical image
analysiscoreattheBRIC.Hewasatenure-trackassistantprofessorattheUniversity
of Pennsylvanian (UPenn), and a faculty member at Johns Hopkins University.
Dr. Shen’s research interests include medical image analysis, computer vision,
and pattern recognition. He has published more than 700 papers for international
journals and conference proceedings. He serves as an editorial board member for
six international journals. Since January of 2012, he has served on the Board of
Directors of The Medical Image Computing and Computer Assisted Intervention
(MICCAI)Society.
MertR.SabuncuisanAssistantProfessorofRadiologyattheA.A.MartinosCenter
forBiomedicalImaging,MassachusettsGeneralHospital,HarvardMedicalSchool,
andaResearchAffiliateatMIT’sComputerScienceandArtificialIntelligenceLab
(CSAIL). Mert completed his postdoctoral training with the medical vision group
at MIT’s CSAIL. He received his PhD degree from the Department of Electrical
Engineering at Princeton University in 2006. Mert’s research aims to develop
computational tools that allow us to draw inferences from biomedical image data,
particularlyinthecontextofneurology,psychiatry,andneuroscience,aswellasfrom
relationshipswithotherdatamodalities,suchasgeneticsandclinicalvariables.Heis
interestedintheapplicationofmachinelearningtechniquesforanalyzinglarge-scale
biomedicaldatasetsandforderivingautomatedimageprocessingpipelinesthatcan
automateand/oraugmentclinicalworkflows.
xix
Preface
Machine learning is playing an increasingly essential role in the field of medical
imaging, including problems as diverse as computer-assisted diagnosis, lesion
detection, segmentation of anatomical regions of interest, brain mapping, spatial
normalizationorregistration,multimodaldatafusion,image-guidedtherapy,image
annotation,anddatabaseretrieval.
Medical images offer an invaluable and rich source of information for a wide
range of clinical and scientific applications. Furthermore, the technologies that
allow us to capture medical images are advancing rapidly, providing higher quality
imagesofpreviouslyunmeasuredbiologicalfeaturesatdecreasingcosts.Thus,inthe
dawning era of data-driven health sciences, image scans will make up a significant
portion of the growing repositories of digital biomedical data. Machine learning,
whichtodatehaslargelyfocusedonnonmedical,artificialintelligenceapplications
suchascomputervision,promisestobeaninstrumentaltoolkitthatwillhelpunleash
thepotentialofmedicalimaging.
The significant variation of anatomy across individuals and the complexities of
theunderlyingbiologyoftenmakeitimpossibletoderiverule-basedorpredesigned
algorithmicsolutionsfortheproblemsweencounterinmedicalimagingapplications.
Therefore learning from prior data to build models about multivariate and dynamic
relationships between variables and utilizingthesemodelstomake inferences isan
indispensablestrategyintacklingthechallengesofmedicalimageanalysis.Machine
learningprovidesanarrayoftoolstoexecutethisstrategy.
Thecentralaimofthisbookistopresentthestate-of-the-artofmachinelearning
methods in medical image analysis. We created this edited volume for medical
doctors, graduate and undergraduate students, academic researchers, and industry
staff who are interested in translating these technologies from the academic setting
to a commercial product. We anticipate that readers interested in this book will
have diverse backgrounds, so we have organized the book into two parts. The first
partsummarizescutting-edgemachinelearningalgorithmsinmedicalimaging.The
topicscoveredinthispartincludenotonlyclassicalprobabilisticmodelingandlearn-
ing methods, but also recent breakthroughs in deep learning, sparse representation/
coding,andbigdatahashing.Eachchapterhasaself-containedandthoroughlitera-
turereview,aswellasanillustrationofhowtoapplytheseideasinmedicalimaging.
In the second part, we have invited leading groups in the world to report their
mostrecentprogress,coveringawidespectrumofmachinelearningmethodsalong
withtheirapplicationtodifferentmedicalimagingmodalities,clinicaldomains,and
organs.Thebiomedicalimagingmodalitiesinthisbookincludeultrasound,magnetic
resonance imaging (MRI), computed tomography (CT), histology, and microscopy
images. The targeted organs span the lung, liver, brain, and prostate, while there
is also a treatment examining genetic associations. We roughly categorized the
applications covered in this book into five topics, which include computer-assisted
diagnosis, image-guided radiation therapy, landmark detection, imaging genomics,
xxi