2024
Patient-Specific Heart Geometry Modeling for Solid Biomechanics Using Deep Learning
Pak D, Liu M, Kim T, Liang L, Caballero A, Onofrey J, Ahn S, Xu Y, McKay R, Sun W, Gleason R, Duncan J. Patient-Specific Heart Geometry Modeling for Solid Biomechanics Using Deep Learning. IEEE Transactions On Medical Imaging 2024, 43: 203-215. PMID: 37432807, PMCID: PMC10764002, DOI: 10.1109/tmi.2023.3294128.Peer-Reviewed Original ResearchMeSH KeywordsBiomechanical PhenomenaComputer SimulationDeep LearningHeartHumansPatient-Specific ModelingConceptsFinite element analysisDeep learning methodsSpatial accuracyElement analysisDeep learningStress estimationLearning methodsSimulation accuracyDeployment simulationHigh spatial accuracyThin structuresMesh generationVolumetric meshingDeformation energyGeometry modelingVolumetric meshMesh qualityElement qualitySimultaneous optimizationMain noveltyBiomechanics studiesMeshModeling characteristicsAccuracyDownstream analysis
2016
Machine learning–based 3‐D geometry reconstruction and modeling of aortic valve deformation using 3‐D computed tomography images
Liang L, Kong F, Martin C, Pham T, Wang Q, Duncan J, Sun W. Machine learning–based 3‐D geometry reconstruction and modeling of aortic valve deformation using 3‐D computed tomography images. International Journal For Numerical Methods In Biomedical Engineering 2016, 33 PMID: 27557429, PMCID: PMC5325825, DOI: 10.1002/cnm.2827.Peer-Reviewed Original ResearchMeSH KeywordsAortic ValveComputer SimulationFinite Element AnalysisHumansImaging, Three-DimensionalMachine LearningTomography, X-Ray ComputedConceptsHuman expertsGeometry reconstructionHuman errorMean discrepancyPreoperative planning systemComputational modeling processReconstructed geometryFinite element model generationModel generationPatient-specific computational modelingCardiac imagesComputational modeling methodsFast feedbackComputational modeling frameworkModeling processMesh correspondencePlanning systemModeling methodMachineModeling frameworkAortic valveImagesDisease diagnosisLarge patient cohortIndividual patient needs
2012
A Dynamical Appearance Model Based on Multiscale Sparse Representation: Segmentation of the Left Ventricle from 4D Echocardiography
Huang X, Dione DP, Compas CB, Papademetris X, Lin BA, Sinusas AJ, Duncan JS. A Dynamical Appearance Model Based on Multiscale Sparse Representation: Segmentation of the Left Ventricle from 4D Echocardiography. Lecture Notes In Computer Science 2012, 15: 58-65. PMID: 23286114, PMCID: PMC3889160, DOI: 10.1007/978-3-642-33454-2_8.Peer-Reviewed Original ResearchConceptsStatistical modelAppearance modelSparse representationIntensity modelPriorsShape predictionRepresentation methodMultiscale sparse representationSpatio-temporal coherenceSparse representation methodLocal appearanceImage sequencesModelRepresentationAppearance informationCoherenceRobustnessSegmentation accuracy
2011
Segmentation of 3D radio frequency echocardiography using a spatio-temporal predictor
Pearlman PC, Tagare HD, Lin BA, Sinusas AJ, Duncan JS. Segmentation of 3D radio frequency echocardiography using a spatio-temporal predictor. Medical Image Analysis 2011, 16: 351-360. PMID: 22078842, PMCID: PMC3267850, DOI: 10.1016/j.media.2011.09.002.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsComputer SimulationDogsEchocardiography, Three-DimensionalHeart VentriclesImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalModels, CardiovascularModels, StatisticalPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueConceptsLeft ventricular endocardial boundarySpatio-temporal predictorsStandard level setRF dataSpatio-temporal coherenceNeighboring framesImage sequencesBoundary detectionMultiple framesImage inhomogeneitySegmentationEndocardial boundaryGeometric constraintsManual tracingRF ultrasoundAlgorithmLevel setsEchocardiographic imagesFrameConditional modelLinear predictorTrackingSpatial modelImagesRobustness3-D Reconstruction of Microtubules from Multi-Angle Total Internal Reflection Fluorescence Microscopy Using Bayesian Framework
Yang Q, Karpikov A, Toomre D, Duncan JS. 3-D Reconstruction of Microtubules from Multi-Angle Total Internal Reflection Fluorescence Microscopy Using Bayesian Framework. IEEE Transactions On Image Processing 2011, 20: 2248-2259. PMID: 21324778, DOI: 10.1109/tip.2011.2114359.Peer-Reviewed Original ResearchConceptsEvanescent fieldDifferent penetration depthsTIRF imagesPenetration depthSamples of microtubulesLaser beamTotal internal reflection fluorescence microscopyAxial resolutionReflection fluorescence microscopyLarge radiusIncident angleMulti-angle total internal reflection fluorescence microscopyElectron microscopy imagesTIRF dataSmall radiusTracking of microtubulesMicroscopy imagesFluorescence microscopyMicroscopyRadiusReconstruction resultsCurvilinear characteristicsZ-dimensionExperimental calibrationMicrotubule curvatureA Unified Framework for Joint Segmentation, Nonrigid Registration and Tumor Detection: Application to MR-Guided Radiotherapy
Lu C, Chelikani S, Duncan JS. A Unified Framework for Joint Segmentation, Nonrigid Registration and Tumor Detection: Application to MR-Guided Radiotherapy. Lecture Notes In Computer Science 2011, 22: 525-537. PMID: 21761683, PMCID: PMC3889153, DOI: 10.1007/978-3-642-22092-0_43.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsArtificial IntelligenceComputer SimulationFemaleHumansImage EnhancementImage Interpretation, Computer-AssistedModels, BiologicalModels, StatisticalPattern Recognition, AutomatedRadiotherapy, Computer-AssistedReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueUterine Cervical NeoplasmsAn Expectation Maximization Based Method for Subcellular Particle Tracking Using Multi-angle TIRF Microscopy
Liang L, Shen H, De Camilli P, Toomre DK, Duncan JS. An Expectation Maximization Based Method for Subcellular Particle Tracking Using Multi-angle TIRF Microscopy. Lecture Notes In Computer Science 2011, 14: 629-636. PMID: 22003671, PMCID: PMC3648983, DOI: 10.1007/978-3-642-23623-5_79.Peer-Reviewed Original Research
2010
3D Radio Frequency Ultrasound Cardiac Segmentation Using a Linear Predictor
Pearlman PC, Tagare HD, Sinusas AJ, Duncan JS. 3D Radio Frequency Ultrasound Cardiac Segmentation Using a Linear Predictor. Lecture Notes In Computer Science 2010, 13: 502-509. PMID: 20879268, PMCID: PMC3889143, DOI: 10.1007/978-3-642-15705-9_61.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsComputer SimulationDogsEchocardiography, Three-DimensionalImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalLinear ModelsModels, CardiovascularMyocardial InfarctionPattern Recognition, AutomatedRadio WavesReproducibility of ResultsSensitivity and SpecificityConceptsLeft ventricular endocardial boundaryStandard level setSpatio-temporal coherenceCardiac segmentationBoundary detectionImage inhomogeneityEndocardial boundarySegmentationGeometric constraintsManual tracingRadio frequency ultrasoundLinear predictorLevel setsRF dataEchocardiographic imagesB-mode dataTrackingImagesDataConstraintsSetDetection
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. 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
2008
Bidirectional Segmentation of Three-Dimensional Cardiac MR Images Using a Subject-Specific Dynamical Model
Zhu Y, Papademetris X, Sinusas AJ, Duncan JS. Bidirectional Segmentation of Three-Dimensional Cardiac MR Images Using a Subject-Specific Dynamical Model. Lecture Notes In Computer Science 2008, 11: 450-457. PMID: 18982636, PMCID: PMC2829658, DOI: 10.1007/978-3-540-85990-1_54.Peer-Reviewed Original ResearchConceptsSubject-specific dynamical modelGeneric dynamical modelDynamical modelSpecific dynamical modelLocal shape variationsDynamic prediction algorithmCardiac sequenceStatistical modelStatic modelModel-based segmentationCardiac dynamicsPeriodic natureMotionCardiac motionAlgorithmShape variationSegmentation errorsErrorModelOne-outPrediction algorithmPropagationCardiac MR imagesCertain framesSegmentation results
2007
Boundary element method-based regularization for recovering of LV deformation
Yan P, Sinusas A, Duncan JS. Boundary element method-based regularization for recovering of LV deformation. Medical Image Analysis 2007, 11: 540-554. PMID: 17584521, DOI: 10.1016/j.media.2007.04.007.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsComputer SimulationDogsElasticityFinite Element AnalysisHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalMagnetic Resonance ImagingModels, CardiovascularReproducibility of ResultsSensitivity and SpecificityStress, MechanicalVentricular Dysfunction, LeftConceptsBoundary element methodImage sequencesElement methodDisplacement fieldDense displacement fieldNew regularization modelDeformationRegularization modelCardiac magnetic resonance image sequencesMagnetic resonance image sequencesEchocardiographic image sequencesLattice densityFeature informationComputation timePhysical plausibilityImage dataDisplacementMatching strategy
2005
A Boundary Element-Based Approach to Analysis of LV Deformation
Yan P, Lin N, Sinusas AJ, Duncan JS. A Boundary Element-Based Approach to Analysis of LV Deformation. Lecture Notes In Computer Science 2005, 8: 778-785. PMID: 16685917, DOI: 10.1007/11566465_96.Peer-Reviewed Original Research
2004
3D image segmentation of deformable objects with joint shape-intensity prior models using level sets
Yang J, Duncan JS. 3D image segmentation of deformable objects with joint shape-intensity prior models using level sets. Medical Image Analysis 2004, 8: 285-294. PMID: 15450223, PMCID: PMC2832842, DOI: 10.1016/j.media.2004.06.008.Peer-Reviewed Original ResearchConceptsImage segmentationImage gray levelsPoint distribution modelObject shapeGray levelsExplicit point correspondencesImage gray level valuesGray level valuesMedical imagesInput imageTraining imagesGray-level variationsMultidimensional dataTraining phaseDeformable objectsPoint correspondencesSegmentationMap shapePrior knowledgePrior informationLevel set functionPrior modelEstimation modelImagesObjectsNeighbor-Constrained Segmentation With Level Set Based 3-D Deformable Models
Yang J, Staib LH, Duncan JS. Neighbor-Constrained Segmentation With Level Set Based 3-D Deformable Models. IEEE Transactions On Medical Imaging 2004, 23: 940-948. PMID: 15338728, PMCID: PMC2838450, DOI: 10.1109/tmi.2004.830802.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsBrainComputer SimulationElasticityHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalInformation Storage and RetrievalMagnetic Resonance ImagingModels, BiologicalModels, StatisticalNumerical Analysis, Computer-AssistedPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificitySignal Processing, Computer-AssistedConceptsThree-dimensional medical imagesImage gray level informationGray level informationPoint distribution modelMedical imagesNeighbor objectsTraining imagesMedical imageryMultiple objectsDeformable modelObject shapeSynthetic dataLevel informationSegmentationMap shapeEstimation frameworkPosition relationshipPrior informationLevel set functionObjectsJoint probability distributionSet functionNeighboring shapesInformationImagesGeometric strategies for neuroanatomic analysis from MRI
Duncan JS, Papademetris X, Yang J, Jackowski M, Zeng X, Staib LH. Geometric strategies for neuroanatomic analysis from MRI. NeuroImage 2004, 23: s34-s45. PMID: 15501099, PMCID: PMC2832750, DOI: 10.1016/j.neuroimage.2004.07.027.Peer-Reviewed Original ResearchConceptsApplied mathematical approachWhite matter fiber tracksStatistical estimationMathematical approachFunction-structure analysisMagnetic resonance imagesEvolution strategyGeometric constraintsImage processingIntersubject registrationRich setGeometric strategyOngoing workData setsUse of levelsCommon spaceNeuroanatomic analysisSetRegistrationFiber tracksHuman brainResonance imagesInformationSegmentationEstimation
2003
Neighbor-Constrained Segmentation with 3D Deformable Models
Yang J, Staib LH, Duncan JS. Neighbor-Constrained Segmentation with 3D Deformable Models. Lecture Notes In Computer Science 2003, 18: 198-209. PMID: 15344458, DOI: 10.1007/978-3-540-45087-0_17.Peer-Reviewed Original ResearchConceptsImage gray level informationGray level informationNeighbor objectsMedical imagesTraining imagesMedical imageryMultiple objectsDeformable modelSynthetic dataLevel informationSegmentationMap shapeEstimation frameworkPrior informationLevel set functionObjectsJoint probability distributionSet functionInformationImagesNovel methodMaximum AJoint density functionProbability distributionFramework
2000
Point-tracked quantitative analysis of left ventricular surface motion from 3-D image sequences
Shi P, Sinusas AJ, Constable RT, Ritman E, Duncan JS. Point-tracked quantitative analysis of left ventricular surface motion from 3-D image sequences. IEEE Transactions On Medical Imaging 2000, 19: 36-50. PMID: 10782617, DOI: 10.1109/42.832958.Peer-Reviewed Original Research