Statistical Shape Analysis of the Proximal Femur

188 Pages·2014·16.53 MB·English

by  Claudia Lindner

Statistical Shape Analysis of the Proximal Femur:
Development of a Fully Automatic Segmentation
System and its Applications
A thesis submitted to
The University of Manchester
for the degree of
Doctor of Philosophy (PhD)
in the Faculty of Medical and Human Sciences
Claudia Lindner
SCHOOL OF MEDICINE
2014
Contents
Listoffigures 6
Listoftables 9
Abstract 10
Declaration 11
Copyrightstatement 12
Alternativeformat 13
Publications 14
Abouttheauthor 16
Acknowledgements 17
Glossary 19
1 Introduction 20
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.2 Aimandobjectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
1.4 Outlineofthethesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2 Technicalbackground 24
2.1 Statisticalshapeanalysis . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.1.1 Definingshape . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.1.2 StatisticalShapeModels . . . . . . . . . . . . . . . . . . . . . . 26
2.1.3 Automaticshapeannotation . . . . . . . . . . . . . . . . . . . . 30
2.2 Conceptsofmachinelearning . . . . . . . . . . . . . . . . . . . . . . . 31
2.2.1 Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.2.2 Typesoflearningproblems . . . . . . . . . . . . . . . . . . . . . 32
2.2.3 Validationoflearning . . . . . . . . . . . . . . . . . . . . . . . . 33
2.2.4 Learningwithdecisiontrees . . . . . . . . . . . . . . . . . . . . 35
2.3 Shapemodelmatching . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.3.1 Featurepointdetection . . . . . . . . . . . . . . . . . . . . . . . 37
2.3.2 ActiveShapeModels . . . . . . . . . . . . . . . . . . . . . . . . 38
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Contents
2.3.3 ConstrainedLocalModels . . . . . . . . . . . . . . . . . . . . . 40
2.3.4 Modelinitialisation . . . . . . . . . . . . . . . . . . . . . . . . . 42
3 Clinicalbackground 45
3.1 Hiposteoarthritis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.1.1 Anatomyofthehipjoint . . . . . . . . . . . . . . . . . . . . . . 46
3.1.2 Signsofdisease . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.2 Complexdiseasegenetics . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.2.1 Basicconceptsandterminology . . . . . . . . . . . . . . . . . . 50
3.2.2 Quantitativetraitassociationanalysis . . . . . . . . . . . . . . . 51
3.3 Project-specificbackground . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.3.1 ThearcOGENConsortium . . . . . . . . . . . . . . . . . . . . . 54
3.3.2 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4 Developmentandquantitativeevaluationofafullyautomaticproximalfemur
segmentationsystem 57
4.1 Preliminarywork: proximalfemurannotationandshapemodelevaluation 58
4.1.1 Landmarkidentification . . . . . . . . . . . . . . . . . . . . . . 58
4.1.2 Manualannotation . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.1.3 Shaperepresentationoftheproximalfemur . . . . . . . . . . . . 62
4.2 Robust and Accurate Shape Model Matching using Random Forest
Regression-Voting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.2.2 Relatedwork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.2.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.2.3.1 RandomForestregression-voting . . . . . . . . . . . . . . . . . 68
4.2.3.2 ConstrainedLocalModels . . . . . . . . . . . . . . . . . . . . . 70
4.2.3.3 RFregression-votingintheCLMframework . . . . . . . . . . . 70
4.2.4 Experimentalevaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.2.4.1 EvaluationofRFregression-votingusingradiographsofthehand 73
4.2.4.2 EvaluationofRFregression-votingusingimagesoftheface . . . 79
4.2.5 Discussionandconclusions . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.3 Fully Automatic Segmentation of the Proximal Femur using Random
ForestRegression-Voting . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
4.3.2 Relatedwork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.3.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.3.3.1 RandomForestregression-voting . . . . . . . . . . . . . . . . . 93
4.3.3.2 ConstrainedLocalModels . . . . . . . . . . . . . . . . . . . . . 94
4.3.4 Fullyautomaticsegmentationsystem . . . . . . . . . . . . . . . . . . . . 95
4.3.4.1 Objectdetection . . . . . . . . . . . . . . . . . . . . . . . . . . 96
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Contents
4.3.4.2 Objectsegmentation . . . . . . . . . . . . . . . . . . . . . . . . 98
4.3.4.3 Combinedsystem . . . . . . . . . . . . . . . . . . . . . . . . . . 99
4.3.5 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
4.3.6 Experimentalevaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 100
4.3.6.1 Globalsearch: automaticfemurdetection . . . . . . . . . . . . . 101
4.3.6.2 Localsearch: accuratefemursegmentation . . . . . . . . . . . . 103
4.3.6.3 Fullsearch: accurateautomaticfemursegmentation . . . . . . . . 105
4.3.7 Discussionandconclusions . . . . . . . . . . . . . . . . . . . . . . . . . 110
4.3.AChoiceofparameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
5 Application of the fully automatic segmentation system to the analysis of
proximalfemurshape 114
5.1 Development of a Fully Automatic Shape Model Matching (FASMM)
System to Derive Statistical Shape Models from Radiographs: Applica-
tion to the Accurate Capture and Global Representation of Proximal Fe-
murShape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
5.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.1.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
5.1.2.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
5.1.2.2 TheFASMM system . . . . . . . . . . . . . . . . . . . . . . . . 118
5.1.2.3 Calculationofgeometricmeasurementsfromthepointsplacedby
theFASMM system . . . . . . . . . . . . . . . . . . . . . . . . . 120
5.1.2.4 Prediction of geometric measurements from the SSM generated
bytheFASMM system . . . . . . . . . . . . . . . . . . . . . . . 121
5.1.2.5 Malevs. femaleproximalfemurshape . . . . . . . . . . . . . . 121
5.1.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
5.1.3.1 ApplicationoftheFASMM system . . . . . . . . . . . . . . . . . 122
5.1.3.2 Comparisonofmanualvs. automaticallygeneratedmeasurements 122
5.1.3.3 Comparison of manual measurements vs. those predicted from
theSSM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
5.1.3.4 Comparisonofmalevs. femaleproximalfemurshape . . . . . . 126
5.1.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
5.2 Increasing Shape Modelling Accuracy by Adjusting for Subject Position-
ing: An Application to the Analysis of Radiographic Proximal Femur
AsymmetryusingDatafromtheOsteoarthritisInitiative . . . . . . . . . . 129
5.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
5.2.2 Materialsandmethods . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
5.2.2.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
5.2.2.2 TheFASMM system . . . . . . . . . . . . . . . . . . . . . . . . 132
5.2.2.3 Adjustingforsubjectpositioning . . . . . . . . . . . . . . . . . . 134
5.2.2.4 AnalysingglobalproximalfemursymmetryusingSSMs . . . . . 134
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Contents
5.2.2.5 Analysing the impact of subject-positioning on hip geometric
measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
5.2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
5.2.3.1 PerformanceoftheFASMM system . . . . . . . . . . . . . . . . 135
5.2.3.2 Adjustingforsubjectpositioning . . . . . . . . . . . . . . . . . 135
5.2.3.3 AnalysingglobalproximalfemursymmetryusingSSMs . . . . . 137
5.2.3.4 Analysing the impact of subject-positioning on hip geometric
measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
5.2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
5.2.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
6 Investigation of Association between Hip Osteoarthritis Susceptibility Loci
andRadiographicProximalFemurShape 144
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
6.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
6.2.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
6.2.2 CandidateSNPselection . . . . . . . . . . . . . . . . . . . . . . 149
6.2.3 StatisticalShapeModels . . . . . . . . . . . . . . . . . . . . . . 149
6.2.4 UnivariateSNPassociationanalysis . . . . . . . . . . . . . . . . 150
6.2.5 MultivariateSNPassociationanalysis . . . . . . . . . . . . . . . 150
6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
6.3.1 CandidateSNPsassociatedwithhipOAorhipmorphology . . . 151
6.3.2 StatisticalShapeModelsoftheproximalfemur . . . . . . . . . . 156
6.3.3 SNPassociationanalysisusingunivariateshapedata . . . . . . . 156
6.3.4 SNPassociationanalysisusingmultivariateshapedata . . . . . . 158
6.4 Discussionandconclusions . . . . . . . . . . . . . . . . . . . . . . . . . 160
6.A Shapemodelmodesformultivariateresults . . . . . . . . . . . . . . . . 163
7 Generaldiscussion 164
7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
7.2 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
7.3 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
8 Conclusionsandfuturework 170
References 171
Appendices 187
A.1 Radiographicmeasurementsofthehip . . . . . . . . . . . . . . . . . . . 187
A.2 ExampleimagesfromthearcOGENdataset . . . . . . . . . . . . . . . . 188
Finalwordcount: 66195
5
List of figures
1.1 Radiographicshapeoftheproximalfemuroutlinedby65points . . . . . . . 21
2.1 ApplyingPCAto2Dvectors . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.2 Firstthreeshapemodesofvariationinproximalfemurmorphology . . . . . . 29
2.3 Example decision tree where internal nodes are represented by circles and
leafnodesbysquares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.4 Example decision tree ensemble where votes from all trees in the ensemble
arecollatedandaveraged . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.5 Illustrationofpoint-to-point(dP)andpoint-to-curve(dC)distances . . . . . . 38
2.6 Two examples of where an insufficient initialisation in position, orientation
or scale causes a grey-level model based ASM to get stuck in a local
optimum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.7 Two examples of CLM model matching results highlighting the sensitivity
totheinitialisationinposition,orientationorscale . . . . . . . . . . . . . . . 42
3.1 Schematicoverviewofriskfactorsassociatedwithosteoarthritis . . . . . . . 46
3.2 Anatomyofthehipjoint . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.3 Radiographicsignsassociatedwithhiposteoarthritis . . . . . . . . . . . . . 48
3.4 Schematicrepresentationofthecontributionofthegenotypeatasingle-SNP
QTLtothedistributionofthevaluesofaquantitativetrait . . . . . . . . . . . 51
3.5 The effect of interaction between alleles on the phenotype in additive,
dominantandrecessivegeneticmodels . . . . . . . . . . . . . . . . . . . . . 53
3.6 Schematic overview of how SNPs serve as markers for QTLs in genetic
associationstudies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.1.1 Illustration of the effect of 2D projection on the appearance of the
trochantersandthemedialproximalfemoralshaftinpelvicradiographs . . . 58
4.1.2 Landmarkannotationoftheproximalfemur . . . . . . . . . . . . . . . . . . 59
4.1.3 FemoralheadexamplesfromthearcOGENdataset . . . . . . . . . . . . . . 61
4.2.1 Superpositionofhistogramsofvotesfora17-pointfacemodel . . . . . . . . 68
4.2.2 Example17-pointmodelcapturingfeaturesoftheface . . . . . . . . . . . . 68
4.2.3 RF regression-voting combines multiple independent predictions. During
searcheachrectangularpatchpredictspositionsforthetargetpoint . . . . . . 69
4.2.4 Iterative model matching procedure to estimate the shape and pose
parameters in the reference frame, given a set of feature point-based cost
6
Listoffigures
images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.2.5 Annotation example for the joints of the hand using 37 points, and the first
twomodesoftheshapemodel . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.2.6 Evaluationoftheeffectofintroducingasecondsearchstagetolocallyrefine
allpointplacements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.2.7 Evaluation of the effect of combining multiple independent estimates of
position by varying the number of trees in the RFs when every tree either
combinesvotesfrommultipleregionsaroundthefeaturepointorvotesonly
fromthecurrentposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
4.2.8 Comparison of fully automatic hand annotation results with different shape
modelmatchingtechniquesandpreviouslypublishedresults . . . . . . . . . 79
4.2.9 Optimisation of the patch and frame width for 2-, 4- and 17-point RFRV-
CLMswithoneandtwostages . . . . . . . . . . . . . . . . . . . . . . . . . 83
4.2.10 Performance evaluation of combinations of optimal 2-, 4- and 17-point
models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4.2.11 Evaluationofvotingstylesforachained2c-17c-17f-pointRFRV-CLM . . . . 85
4.2.12 Comparison of the performance of a chained 2c-17c-17f-point RFRV-CLM,
evaluatedontheBioIDdataset,withpreviouslypublishedresults . . . . . . . 86
4.2.13 Example images from the BioID dataset showing the automatic annotations
generated by a chained 2c-17c-17f-point RFRV-CLM, initialised using a
HoughForesttolocatethepupilcentres . . . . . . . . . . . . . . . . . . . . 86
4.2.14 Effect of varying the number of trees in the RFs for the 2c-point and 17c-
pointstagesofachained2c-17c-17f-pointRFRV-CLM . . . . . . . . . . . . 88
4.3.1 Iterativeproceduretoupdateshapeandposeparameters . . . . . . . . . . . . 96
4.3.2 Algorithmicsummaryofthefullyautomaticsegmentationsystem . . . . . . 97
4.3.3 Segmentationoftheproximalfemur . . . . . . . . . . . . . . . . . . . . . 100
4.3.4 Visualisationofglobalsearchresultsofthefulldetector . . . . . . . . . . . 102
4.3.5 Globalsearchresultsforallthreedetectors . . . . . . . . . . . . . . . . . . 103
4.3.6 Local search results when start searching from the mean shape at the true
pose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
4.3.7 Fully automatic search results compared to local search results for the two-
stage approach using the coarse model followed by a single search iteration
of the fine model as well as for the one-stage approach using the coarse
modelonly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.3.8 Segmentation issueswhenthe lessertrochanteris eitherverywell visibleor
hardlyvisible . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
4.3.9 Fully automatic search results compared to previously best published
results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
4.3.10 Fullyautomaticsearchresultscomparinggender-specificandmixed-gender
localmodels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
7
Listoffigures
4.3.11 Examplesofsegmentationresultsofthefullyautomaticsystem . . . . . . . 109
5.1.1 Fullyautomaticsegmentationoftheproximalfemur . . . . . . . . . . . . . 119
5.1.2 GeometricmeasurementsbasedontheoutputoftheFASMM system . . . . 120
5.1.3 Bland-Altman plots for manual measurements vs. fully automatic measure-
ments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
5.1.4 Bland-Altman plots for manual measurements vs. predicted measurements
usinglinearregressionbasedonshapemodevalues . . . . . . . . . . . . . 125
5.1.5 Shapedifferencesbetweenmaleandfemaleproximalfemurshapebasedon
fullyautomaticsearchresultsfor1,105images . . . . . . . . . . . . . . . . 126
5.2.1 Segmentation examples showing the single and combined proximal femur
models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
5.2.2 Twomodesofthecombinedproximalfemurmodelshowclearoppositional
asymmetric patterns of shape variation for the left and right proximal
femurs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
5.2.3 Modes 1, 2, 4, 5 and 7 of the combined proximal femur model show
approximately symmetric patterns of shape variation for the left and right
proximalfemurs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
5.2.4 Theeffectofexcludingmodes3and6fromthecombinedSSM . . . . . . . 138
5.2.5 Analysis of proximal femur symmetry (using the left and right femurs of
1,258CaucasianfemalesfromtheOAIdataset) . . . . . . . . . . . . . . . 139
6.1 Overview of study subject and single nucleotide polymorphism (SNP)
selectionfromthearcOGENdatasets . . . . . . . . . . . . . . . . . . . . . 148
6.2 Annotationexampleoutliningtheproximalfemurusing65points . . . . . 148
6.3 Visualisation of the familywise p-values for all 492 tests (41 SNPs,
12 SSM modes) for association between univariate SSM modes of the
proximalfemurandpreviouslyreportedhiposteoarthritisorproximalfemur
morphologysusceptibilitySNPs . . . . . . . . . . . . . . . . . . . . . . . 157
6.4 Shape model modes associated with candidate SNPs in this study from the
univariateandmultivariateanalyses . . . . . . . . . . . . . . . . . . . . . 158
6.A.1 Shape model modes of multivariate results for the three most contributing
modesperSNP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
7.1 Two examples of RFRV-CLM model matching results highlighting the
excellentperformanceoftheRFRV-CLMapproach . . . . . . . . . . . . . 165
7.2 Fully automatic search results for the proximal femur when including the
trochantersaswellasforthekneejoint . . . . . . . . . . . . . . . . . . . . 166
7.3 Examples of search results when applying the fully automatic system that
wastrainedonOA-unaffectedproximalfemurstoOA-affectedfemurs . . . 167
A.1 Selectedradiographicmeasurementsoftheproximalfemur . . . . . . . . . 187
A.2 Example images from the arcOGEN dataset showing the variation in image
qualityaswellasintheposeoftheproximalfemur . . . . . . . . . . . . . 188
8
List of tables
4.1.1 Summary of the 65 landmark positions of the proximal femur used in the
developmentofthefullyautomaticproximalfemursegmentationsystem . . . 60
4.1.2 ResidualanalysisofSSMmodesrepresentingproximalfemurshape . . . . . 62
4.2.1 Effect of sampling regional patches on a coarse grid rather than at every
pixel. Fullyautomaticannotationresultsusingatwo-stage1-10-treesmodel
where the step size of the first-stage is fixed to 3 and the step size of the
secondstageisvaried . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.2.2 Optimalvaluesforallmulti-stageRFRV-CLMparameters . . . . . . . . . . . 81
4.3.1 Selectedparametersforthelocalsearchmodels . . . . . . . . . . . . . . . 104
5.1.1 Manual inter-observer and fully automatic vs. manual repeatability for hip
geometricmeasurements . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
5.2.1 Summarystatisticsforthe1,282subjectsincludedinthisstudy . . . . . . . 133
5.2.2 Analysis of proximal femur symmetry based on 1,258 Caucasian females
fromtheOAIdataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
6.1 Summary of SNPs associated with hip OA or proximal femur
morphology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
6.2 CandidatearcOGENSNPsselectedforthisstudy . . . . . . . . . . . . . . 155
6.3 Percentage of shape variation explained by the modes of the mixed-gender,
femaleandmaleshapemodels . . . . . . . . . . . . . . . . . . . . . . . . 156
6.4 Association between univariate SSM modes of the proximal femur and
previouslyreportedhiposteoarthritisormorphologysusceptibilitySNPs . . 157
6.5 Absolute loading values of individual SSM modes for the five SNPs that
showedthemostsignificantassociationinthemultivariateanalysis . . . . . 159
9
Abstract
Osteoarthritis (OA) is the most common form of human joint disease causing signifi-
cant pain and disability. Current treatment for hip OA is limited to pain management and
joint replacement for end-stage disease. The development of methods for early diagnosis
andnewtreatmentoptionsareurgentlyneededtominimisetheimpactofthedisease.
StudiesofhipOAhaveshownthathipjointmorphologycorrelateswithsusceptibility
tohipOAanddiseaseprogression. Boneshapeanalysesplayanimportantroleindisease
diagnosis, pre-operative planning, and treatment analysis as well as in epidemiological
studiesaimedat identifyingriskfactorsfor hipOA.StatisticalShapeModels (SSMs)are
beingincreasinglyappliedtoimaging-basedboneshapeanalysesastheyprovideameans
of quantitatively describing the global shape of the bone. This is in contrast to conven-
tionalclinicalandresearchpracticewheretheanalysisofboneshapeisreducedtoaseries
ofmeasurementsoflengthsandangles.
Thisthesisdescribesthedevelopmentofanovelfullyautomaticsoftwaresystemthat
segments the proximal femur from anteroposterior (AP) pelvic radiographs by densely
placing 65 points along its contour. These annotations can then be used for the detailed
morphometric analysis of proximal femur shape. The performance of the system was
evaluatedonalargedatasetof839radiographsofmixedquality. Achievingameanpoint-
to-curve error of less than 0.9mm for 99% of all 839 AP pelvic radiographs, this is the
most accurate and robust automatic method for segmenting the proximal femur in two-
dimensionalradiographsyetpublished.
The system was also applied to a number of morphometric analyses of the proximal
femur, showing that SSM-based radiographic proximal femur shape significantly differs
between males and females, and is highly symmetric between the left and right hip joint
of an individual. In addition, the research described in this thesis demonstrates how the
point annotations resulting from the system can be used for univariate and multivariate
geneticassociationanalyses,identifyingthreenovelgeneticvariantsthatcontributetora-
diographicproximalfemurshapewhilealsoshowinganassociationwithhipOA.
The developed system will facilitate complex morphometric and genetic analyses of
shape variation of the proximal femur across large datasets, paving the way for the devel-
opmentofnewoptionstodiagnose,treatandpreventhipOA.
Institution: TheUniversityofManchester
Candidate: ClaudiaLindner
Degreetitle: DoctorofPhilosophy
Thesistitle: StatisticalShapeAnalysisoftheProximalFemur: Development
ofaFullyAutomaticSegmentationSystemanditsApplications
Date: June2014
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