2019
Flow network tracking for spatiotemporal and periodic point matching: Applied to cardiac motion analysis
Parajuli N, Lu A, Ta K, Stendahl J, Boutagy N, Alkhalil I, Eberle M, Jeng GS, Zontak M, O'Donnell M, Sinusas AJ, Duncan JS. Flow network tracking for spatiotemporal and periodic point matching: Applied to cardiac motion analysis. Medical Image Analysis 2019, 55: 116-135. PMID: 31055125, PMCID: PMC6939679, DOI: 10.1016/j.media.2019.04.007.Peer-Reviewed Original ResearchConceptsDeformation/strainExcellent tracking accuracyEntire cardiac cycleTracking accuracyCardiac motion analysisAccurate estimationSurface pointsEchocardiographic image sequencesLV motionDisplacementMotion analysisImage sequencesCardiac cyclePoint matchingMotionConsecutive framesEstimationNetwork trackingImportant characteristicsSignificant promiseSchemeGood correlationFlow
2013
Contour tracking in echocardiographic sequences via sparse representation and dictionary learning
Huang X, Dione DP, Compas CB, Papademetris X, Lin BA, Bregasi A, Sinusas AJ, Staib LH, Duncan JS. Contour tracking in echocardiographic sequences via sparse representation and dictionary learning. Medical Image Analysis 2013, 18: 253-271. PMID: 24292554, PMCID: PMC3946038, DOI: 10.1016/j.media.2013.10.012.Peer-Reviewed Original ResearchConceptsContour trackerSparse representationEchocardiographic sequencesRegion-based level set segmentationLevel set segmentationLocal image appearanceManual tracingExpert manual tracingsMultiscale sparse representationImage sequencesSegmentation resultsAppearance modelSpatiotemporal priorsFirst frameMultilevel informationHuman data setsEjection fraction estimatesLocal appearanceImage appearanceDictionary learningShape modelContour trackingManual resultsData setsContour estimation
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 modelImagesRobustnessSegmentation of 3D RF Echocardiography Using a Multiframe Spatio-temporal Predictor
Pearlman PC, Tagare HD, Lin BA, Sinusas AJ, Duncan JS. Segmentation of 3D RF Echocardiography Using a Multiframe Spatio-temporal Predictor. Lecture Notes In Computer Science 2011, 22: 37-48. PMID: 21761644, DOI: 10.1007/978-3-642-22092-0_4.Peer-Reviewed Original ResearchConceptsLeft ventricular endocardial boundarySpatio-temporal predictorsStandard level setSpatio-temporal coherenceNeighboring framesImage sequencesBoundary detectionRF dataMultiple framesImage inhomogeneitySegmentationEndocardial boundaryGeometric constraintsManual tracingRF ultrasoundLevel setsConditional modelEchocardiographic imagesFrameLinear predictorAlgorithmTrackingSpatial modelImagesRobustness
2008
Segmentation of Left Ventricle from 3D Cardiac MR Image Sequences Using a Subject-Specific Dynamical Model
Zhu Y, Papademetris X, Sinusas A, Duncan JS. Segmentation of Left Ventricle from 3D Cardiac MR Image Sequences Using a Subject-Specific Dynamical Model. 2015 IEEE Conference On Computer Vision And Pattern Recognition (CVPR) 2008, 2008: 1-8. PMID: 20052308, PMCID: PMC2801445, DOI: 10.1109/cvpr.2008.4587433.Peer-Reviewed Original ResearchSubject-specific dynamical modelGeneric dynamical modelDynamical modelSpecific dynamical modelRecursive Bayesian frameworkStatic modelBayesian frameworkStatistical modelCardiac sequenceCardiac MR image sequencesModel-based segmentationSpecific motion patternsCardiac shapeMR image sequencesImage sequencesMotion patternsModelOne-outLocal consistencyCurrent frameExperimental resultsSegmentation resultsDynamicsPast framesInter-subject variability
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
2006
Towards pointwise motion tracking in echocardiographic image sequences – Comparing the reliability of different features for speckle tracking
Yu W, Yan P, Sinusas AJ, Thiele K, Duncan JS. Towards pointwise motion tracking in echocardiographic image sequences – Comparing the reliability of different features for speckle tracking. Medical Image Analysis 2006, 10: 495-508. PMID: 16574465, DOI: 10.1016/j.media.2005.12.003.Peer-Reviewed Original ResearchConceptsMotion trackingBetter compensation resultsRadio frequency signalsLarge deformationDisplacement estimationTissue motionFrequency signalsSmall deformationsRF signalCompensation resultsFiltered featuresTracking featuresDeformationLinear convolution modelExperiment resultsTrackingEchocardiographic image sequencesPhantom examplesReliability measuresImage sequencesB-modeInverse problemSignalsReliabilityDifferent features
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
2003
Combinative multi-scale level set framework for echocardiographic image segmentation
Lin N, Yu W, Duncan JS. Combinative multi-scale level set framework for echocardiographic image segmentation. Medical Image Analysis 2003, 7: 529-537. PMID: 14561556, DOI: 10.1016/s1361-8415(03)00035-5.Peer-Reviewed Original ResearchConceptsLevel set frameworkShape knowledgeTedious human effortsSet frameworkEchocardiographic image sequencesLine training processEchocardiographic image segmentationUltrasound imagesImage segmentationAutomatic segmentationHuman effortImage sequencesBoundary detectionCoarse boundariesEdge featuresTraining processShape templatePoor featuresEndocardial boundarySegmentationContour evolutionRegion homogeneityImagesExperimental resultsCoarse scale
2002
Model-driven brain shift compensation
Škrinjar O, Nabavi A, Duncan J. Model-driven brain shift compensation. Medical Image Analysis 2002, 6: 361-373. PMID: 12494947, DOI: 10.1016/s1361-8415(02)00062-2.Peer-Reviewed Original ResearchConceptsBrain shift compensationShift compensationIntraoperative surface dataIntraoperative brain deformationSpring-mass modelContinuum mechanicsBrain deformationNavigation systemSame coordinate systemDeformationSurface dataBrain shiftMR image sequencesCoordinate systemImage sequencesSurgical navigation systemDominant errorCompensationPartial validationFull volumeSystemErrorEstimation of 3-D Left Ventricular Deformation from Medical Images Using Biomechanical Models
Papademetris* X, Sinusas AJ, Dione DP, Constable RT, Duncan JS. Estimation of 3-D Left Ventricular Deformation from Medical Images Using Biomechanical Models. IEEE Transactions On Medical Imaging 2002, 21: 786. PMID: 12374316, DOI: 10.1109/tmi.2002.801163.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsCoronary DiseaseDogsElasticityFinite Element AnalysisHeart VentriclesHumansImage EnhancementImaging, Three-DimensionalMagnetic Resonance Imaging, CineModels, CardiovascularPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificityStress, MechanicalConceptsDense motion fieldRegional cardiac deformationLinear elastic modelSoft tissue deformationMotion fieldTerms of strainBiomechanical modelDeformation estimationTissue deformationFiber directionDeformationThree-dimensional image sequencesCardiac deformationHeart wallGood agreementHeart deformationGeneric methodologyMuscle fiber directionImage-derived informationImage sequencesEstimationWallSpecific directionQuantitative estimationInitial correspondence