2020
Unsupervised motion tracking of left ventricle in echocardiography
Ahn SS, Ta K, Lu A, Stendahl JC, Sinusas AJ, Duncan JS. Unsupervised motion tracking of left ventricle in echocardiography. Proceedings Of SPIE--the International Society For Optical Engineering 2020, 11319: 113190z-113190z-7. PMID: 32994659, PMCID: PMC7521020, DOI: 10.1117/12.2549572.Peer-Reviewed Original ResearchMotion trackingGround truth displacement fieldsConvolutional neural networkAccurate motion trackingDense displacement fieldB-mode echocardiography imagesU-NetNeural networkTracking frameworkNon-rigid registration algorithmTarget imageRegistration algorithmTarget frameSource frameAlgorithmEchocardiography imagesFavorable performanceDatasetImagesTrackingDisplacement estimationLarge amountEchocardiographic imagesSegmentationNetwork
2014
Objective comparison of particle tracking methods
Chenouard N, Smal I, de Chaumont F, Maška M, Sbalzarini IF, Gong Y, Cardinale J, Carthel C, Coraluppi S, Winter M, Cohen AR, Godinez WJ, Rohr K, Kalaidzidis Y, Liang L, Duncan J, Shen H, Xu Y, Magnusson KE, Jaldén J, Blau HM, Paul-Gilloteaux P, Roudot P, Kervrann C, Waharte F, Tinevez JY, Shorte SL, Willemse J, Celler K, van Wezel GP, Dan HW, Tsai YS, de Solórzano C, Olivo-Marin JC, Meijering E. Objective comparison of particle tracking methods. Nature Methods 2014, 11: 281-289. PMID: 24441936, PMCID: PMC4131736, DOI: 10.1038/nmeth.2808.Peer-Reviewed Original Research
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
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 echocardiographyNon-rigid Registration with Missing Correspondences in Preoperative and Postresection Brain Images
Chitphakdithai N, Duncan JS. Non-rigid Registration with Missing Correspondences in Preoperative and Postresection Brain Images. Lecture Notes In Computer Science 2010, 13: 367-374. PMID: 20879252, PMCID: PMC3031159, DOI: 10.1007/978-3-642-15705-9_45.Peer-Reviewed Original ResearchConceptsTypes of datasetsNon-rigid registration methodImage alignmentNon-rigid registrationMissing correspondencesSegmentation algorithmNon-rigid registration algorithmSimilarity metricCorrespondence problemValid correspondencesRegistration algorithmRegistration methodExpectation-maximization algorithmBrain imagesJoint registrationReal dataAlgorithmImagesRegistrationError kernel
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
2006
Nonrigid 3D Brain Registration Using Intensity/Feature Information
DeLorenzo C, Papademetris X, Wu K, Vives KP, Spencer D, Duncan JS. Nonrigid 3D Brain Registration Using Intensity/Feature Information. Lecture Notes In Computer Science 2006, 9: 932-939. PMID: 17354980, PMCID: PMC2864121, DOI: 10.1007/11866565_114.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsArtificial IntelligenceBayes TheoremBrainEpilepsyHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalMagnetic Resonance ImagingPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueSurgery, Computer-AssistedConceptsStereo camera imagesCamera calibration errorsPreoperative brain imagesSurface tracking algorithmFeature informationCamera imagesTracking algorithmBrain imagesOptimization termSurface detectionRobust modelImagesBrain registrationIntraoperative brainCalibration errorsAlgorithmAccuracyRegistrationModelInformation
2003
Entropy-Based Dual-Portal-to-3-DCT Registration Incorporating Pixel Correlation
Bansal R, Staib LH, Chen Z, Rangarajan A, Knisely J, Nath R, Duncan JS. Entropy-Based Dual-Portal-to-3-DCT Registration Incorporating Pixel Correlation. IEEE Transactions On Medical Imaging 2003, 22: 29. PMID: 12703758, DOI: 10.1109/tmi.2002.806430.Peer-Reviewed Original ResearchConceptsRegistration frameworkImage dataMutual information-based registration algorithmRegistration parametersPortal imagesUltrasound image dataReal patient dataTomography image dataImage pixelsPixel correlationRegistration algorithmPatient setup verificationSegmentationPixel intensityMarkov random processInitial versionTransformation parametersAppropriate entropyImagesAlgorithmPatient dataFrameworkCT imagesLine processSetup verification