2021
Anatomy-Constrained Contrastive Learning for Synthetic Segmentation Without Ground-Truth
Zhou B, Liu C, Duncan J. Anatomy-Constrained Contrastive Learning for Synthetic Segmentation Without Ground-Truth. Lecture Notes In Computer Science 2021, 12901: 47-56. DOI: 10.1007/978-3-030-87193-2_5.Peer-Reviewed Original ResearchSegmentation networkContrastive learningManual segmentationSuperior segmentation performanceObject of interestSynthetic SegmentationManual effortSegmentation performanceTraining dataUnsupervised adaptationImaging dataSource modalitySegmentationNetworkPrevious methodsLearningLarge amountSuccessful applicationPET imaging dataImagesObjectsCodeDataNew imaging modality
2010
A coupled deformable model for tracking myocardial borders from real-time echocardiography using an incompressibility constraint
Zhu Y, Papademetris X, Sinusas AJ, Duncan JS. A coupled deformable model for tracking myocardial borders from real-time echocardiography using an incompressibility constraint. Medical Image Analysis 2010, 14: 429-448. PMID: 20350833, PMCID: PMC4318707, DOI: 10.1016/j.media.2010.02.005.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsArtificial IntelligenceComputer SystemsDogsEchocardiography, Three-DimensionalElasticity Imaging TechniquesHumansImage EnhancementImage Interpretation, Computer-AssistedPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueConceptsDeformable modelImage-derived informationLV endocardial boundariesImage acquisition techniquesFinal segmentationAutomatic algorithmGround truthManual segmentationVolumetric imagesSegmentationSynthetic dataEndocardial boundaryNumber of effortsMyocardial bordersEpicardial boundariesAcquisition techniquesInstantaneous acquisitionConstraintsImagesEchocardiographic imagesSetSpeckle statisticsAlgorithmReal-time echocardiographyIntegrated Segmentation and Nonrigid Registration for Application in Prostate Image-Guided Radiotherapy
Lu C, Chelikani S, Chen Z, Papademetris X, Staib LH, Duncan JS. Integrated Segmentation and Nonrigid Registration for Application in Prostate Image-Guided Radiotherapy. Lecture Notes In Computer Science 2010, 13: 53-60. PMID: 20879214, DOI: 10.1007/978-3-642-15705-9_7.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsHumansImaging, Three-DimensionalMaleProstatic NeoplasmsRadiographic Image EnhancementRadiographic Image Interpretation, Computer-AssistedRadiotherapy, Computer-AssistedReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueSystems IntegrationTomography, X-Ray ComputedConceptsManual segmentationAutomatic segmentationImportant treatment parametersNonrigid registrationImage-guided radiotherapy systemReal patient dataNon-rigid registrationIntegrated SegmentationRegistration partRadiotherapy linear acceleratorSegmentationTreatment imagesImage qualityCone-beam CTTreatment parametersImagesPromising resultsPatient dataKey anatomical structuresLinear acceleratorRegistrationPrevious workRadiotherapy system
2009
A Dynamical Shape Prior for LV Segmentation from RT3D Echocardiography
Zhu Y, Papademetris X, Sinusas AJ, Duncan JS. A Dynamical Shape Prior for LV Segmentation from RT3D Echocardiography. Lecture Notes In Computer Science 2009, 5761: 206-213. PMID: 20054422, PMCID: PMC2801876, DOI: 10.1007/978-3-642-04268-3_26.Peer-Reviewed Original ResearchSubject-specific dynamical modelCurrent frameMotion patternsRecursive Bayesian frameworkSegmentation taskPast framesAutomatic segmentationPrevious frameSegmentation processShape priorsLV segmentationManual segmentationSegmentationIntensity informationCardiac sequenceEchocardiographic sequencesStatic modelPrior knowledgeTemporal coherenceDynamical shape priorsCardiac motionCardiac modelsBayesian frameworkGeneric dynamical modelEchocardiographic imagesA dynamical shape prior for LV segmentation from RT3D echocardiography.
Zhu Y, Papademetris X, Sinusas AJ, Duncan JS. A dynamical shape prior for LV segmentation from RT3D echocardiography. 2009, 12: 206-13. PMID: 20425989, PMCID: PMC7814293.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsArtificial IntelligenceComputer SimulationComputer SystemsEchocardiography, Three-DimensionalHeart VentriclesHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalModels, AnatomicPattern Recognition, AutomatedPhantoms, ImagingReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueConceptsSubject-specific dynamical modelCurrent frameMotion patternsRecursive Bayesian frameworkSegmentation taskPast framesAutomatic segmentationPrevious frameSegmentation processLV segmentationManual segmentationSegmentationIntensity informationCardiac sequenceEchocardiographic sequencesStatic modelPrior knowledgeTemporal coherenceCardiac motionCardiac modelsBayesian frameworkGeneric dynamical modelEchocardiographic imagesFrameInter-subject variability
2007
Segmentation of Myocardial Volumes from Real-Time 3D Echocardiography Using an Incompressibility Constraint
Zhu Y, Papademetris X, Sinusas A, Duncan JS. Segmentation of Myocardial Volumes from Real-Time 3D Echocardiography Using an Incompressibility Constraint. Lecture Notes In Computer Science 2007, 10: 44-51. PMID: 18051042, DOI: 10.1007/978-3-540-75757-3_6.Peer-Reviewed Original ResearchConceptsAutomatic segmentationImage-derived informationLV endocardial boundariesFinal representationManual segmentationSegmentationEndocardial boundaryEpicardial boundariesReal-time 3D echocardiographyTight couplingNew approachThree-dimensional shapeConstraintsVariety of effortsRepresentationInformationNew imaging modalitySet