2020
Layer Embedding Analysis in Convolutional Neural Networks for Improved Probability Calibration and Classification
Zhang F, Dvornek N, Yang J, Chapiro J, Duncan J. Layer Embedding Analysis in Convolutional Neural Networks for Improved Probability Calibration and Classification. IEEE Transactions On Medical Imaging 2020, 39: 3331-3342. PMID: 32356739, PMCID: PMC7606489, DOI: 10.1109/tmi.2020.2990625.Peer-Reviewed Original ResearchConceptsConvolutional neural networkNeural networkClassification taskProbability calibrationTissue classification tasksImage representationBaseline methodsPublic datasetsModel performanceRandom forest modelNetworkBetter performanceForest modelDatasetClassificationTaskCT imagesImagesOriginal model outputMR imagesModel outputInstitutional datasetPerformanceEmbeddingOutputSparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation
Onofrey JA, Staib LH, Huang X, Zhang F, Papademetris X, Metaxas D, Rueckert D, Duncan JS. Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation. Annual Review Of Biomedical Engineering 2020, 22: 1-27. PMID: 32169002, PMCID: PMC9351438, DOI: 10.1146/annurev-bioeng-060418-052147.Peer-Reviewed Original Research
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 ResearchMeSH KeywordsAlgorithmsAnimalsDogsEchocardiographyImage Processing, Computer-AssistedImaging, Three-DimensionalMotionNeural Networks, ComputerVentricular Dysfunction, LeftConceptsDeformation/strainExcellent tracking accuracyEntire cardiac cycleTracking accuracyCardiac motion analysisAccurate estimationSurface pointsEchocardiographic image sequencesLV motionDisplacementMotion analysisImage sequencesCardiac cyclePoint matchingMotionConsecutive framesEstimationNetwork trackingImportant characteristicsSignificant promiseSchemeGood correlationFlow
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
Volumetric Intraoperative Brain Deformation Compensation: Model Development and Phantom Validation
DeLorenzo C, Papademetris X, Staib LH, Vives KP, Spencer DD, Duncan JS. Volumetric Intraoperative Brain Deformation Compensation: Model Development and Phantom Validation. IEEE Transactions On Medical Imaging 2012, 31: 1607-1619. PMID: 22562728, PMCID: PMC3600363, DOI: 10.1109/tmi.2012.2197407.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsBiomechanical PhenomenaBrainGame TheoryHumansImaging, Three-DimensionalModels, NeurologicalPhantoms, ImagingReproducibility of ResultsSkullSurgery, Computer-AssistedConceptsLinear elastic modelSurface displacementsElastic modelBrain deformationVolumetric brain deformationMaterial parametersMaterial propertiesRealistic brain phantomDeformationBiomechanical modelIntraoperative brainModel accuracyLocalization errorPhantom validationAccurate surgical guidanceModel solutionsModel developmentInitial estimationDisplacementModel sensitivityQuantitative validationPhantom resultsPreoperative imagesSurgical guidancePreliminary applicationSimultaneous Nonrigid Registration, Segmentation, and Tumor Detection in MRI Guided Cervical Cancer Radiation Therapy
Lu C, Chelikani S, Jaffray DA, Milosevic MF, Staib LH, Duncan JS. Simultaneous Nonrigid Registration, Segmentation, and Tumor Detection in MRI Guided Cervical Cancer Radiation Therapy. IEEE Transactions On Medical Imaging 2012, 31: 1213-1227. PMID: 22328178, PMCID: PMC3889159, DOI: 10.1109/tmi.2012.2186976.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsFemaleHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalMagnetic Resonance ImagingPattern Recognition, AutomatedRadiotherapy, ConformalRadiotherapy, Image-GuidedReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueUterine Cervical NeoplasmsPrecise Segmentation of Multiple Organs in CT Volumes Using Learning-Based Approach and Information Theory
Lu C, Zheng Y, Birkbeck N, Zhang J, Kohlberger T, Tietjen C, Boettger T, Duncan JS, Zhou SK. Precise Segmentation of Multiple Organs in CT Volumes Using Learning-Based Approach and Information Theory. Lecture Notes In Computer Science 2012, 15: 462-469. PMID: 23286081, DOI: 10.1007/978-3-642-33418-4_57.Peer-Reviewed Original ResearchConceptsMarginal Space LearningCT volumesChallenging segmentation problemInformation-theoretic schemesLearning-based approachComputer-aided diagnosisExcellent segmentation accuracyRobust boundary detectionInformation theoryPelvic organ segmentationSteerable featuresChallenging datasetArt solutionsOrgan segmentationSegmentation problemSpace learningSegmentation performanceSegmentation accuracyPrecise segmentationBoundary detectionJensen-Shannon divergenceTheoretic schemeInference processDiverse sourcesPrevious state
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 modelImagesRobustnessAn integrated approach to segmentation and nonrigid registration for application in image-guided pelvic radiotherapy
Lu C, Chelikani S, Papademetris X, Knisely JP, Milosevic MF, Chen Z, Jaffray DA, Staib LH, Duncan JS. An integrated approach to segmentation and nonrigid registration for application in image-guided pelvic radiotherapy. Medical Image Analysis 2011, 15: 772-785. PMID: 21646038, PMCID: PMC3164526, DOI: 10.1016/j.media.2011.05.010.Peer-Reviewed Original ResearchAlgorithmsBayes TheoremFemaleHumansImaging, Three-DimensionalMaleProstatic NeoplasmsRadiographic Image EnhancementRadiographic Image Interpretation, Computer-AssistedRadiotherapy, Computer-AssistedReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueSystems IntegrationTomography, X-Ray ComputedUterine Cervical Neoplasms3-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 curvature
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 dataTrackingImagesDataConstraintsSetDetectionEstimation of 3D Geometry of Microtubules Using Multi-angle Total Internal Reflection Fluorescence Microscopy
Yang Q, Karpikov A, Toomre D, Duncan J. Estimation of 3D Geometry of Microtubules Using Multi-angle Total Internal Reflection Fluorescence Microscopy. Lecture Notes In Computer Science 2010, 13: 538-545. PMID: 20879357, DOI: 10.1007/978-3-642-15745-5_66.Peer-Reviewed Original ResearchConceptsTotal internal reflection fluorescence microscopyReflection fluorescence microscopyTotal internal reflection fluorescence microscopy imagesPtK2 cellsMicrotubulesFluorescence microscopyTIRF imagesImportant biological parametersTIRF dataFluorescence microscopy imagesMulti-angle total internal reflection fluorescence microscopyBiological parametersMicrotubule curvatureBiological samplesTracking Clathrin Coated Pits with a Multiple Hypothesis Based Method
Liang L, Shen H, De Camilli P, Duncan JS. Tracking Clathrin Coated Pits with a Multiple Hypothesis Based Method. Lecture Notes In Computer Science 2010, 13: 315-322. PMID: 20879330, PMCID: PMC3889144, DOI: 10.1007/978-3-642-15745-5_39.Peer-Reviewed Original ResearchIntegrated 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 systemNon-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
2009
2D‐3D registration for prostate radiation therapy based on a statistical model of transmission images
Munbodh R, Tagare HD, Chen Z, Jaffray DA, Moseley DJ, Knisely JP, Duncan JS. 2D‐3D registration for prostate radiation therapy based on a statistical model of transmission images. Medical Physics 2009, 36: 4555-4568. PMID: 19928087, DOI: 10.1118/1.3213531.Peer-Reviewed Original ResearchAlgorithmsData Interpretation, StatisticalHumansImage Interpretation, Computer-AssistedImaging, Three-DimensionalInformation Storage and RetrievalMalePattern Recognition, AutomatedPhantoms, ImagingProstatic NeoplasmsRadiographic Image EnhancementRadiotherapy, ConformalReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueFrom medical image computing to computer‐aided intervention: development of a research interface for image‐guided navigation
Papademetris X, DeLorenzo C, Flossmann S, Neff M, Vives KP, Spencer DD, Staib LH, Duncan JS. From medical image computing to computer‐aided intervention: development of a research interface for image‐guided navigation. International Journal Of Medical Robotics And Computer Assisted Surgery 2009, 5: 147-157. PMID: 19301361, PMCID: PMC2796181, DOI: 10.1002/rcs.241.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsHumansImaging, Three-DimensionalNeurosurgical ProceduresRoboticsSoftwareSoftware DesignSurgery, Computer-AssistedConceptsResearch interfaceNavigation systemApplication programming interfaceDual computer systemComputer-aided interventionsSurgery navigation systemImage-guided navigation systemProgramming interfaceClient programNetwork interfacesMedical imagesImage-guided navigationResearch softwareReal timeViable solutionSoftwareImage analysis softwareTool positionVersatile linkAnalysis softwareImagesInterfaceNavigationSystemResearch techniquesA 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
Automated 2D–3D registration of portal images and CT data using line‐segment enhancement
Munbodh R, Chen Z, Jaffray DA, Moseley DJ, Knisely JP, Duncan JS. Automated 2D–3D registration of portal images and CT data using line‐segment enhancement. Medical Physics 2008, 35: 4352-4361. PMID: 18975681, PMCID: PMC3910153, DOI: 10.1118/1.2975143.Peer-Reviewed Original ResearchAlgorithmsArtificial IntelligenceHumansImaging, Three-DimensionalMalePattern Recognition, AutomatedProstatic NeoplasmsRadiographic Image EnhancementRadiographic Image Interpretation, Computer-AssistedRadiotherapy, Computer-AssistedReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueTomography, X-Ray ComputedEffects of the Brain-Derived Neurotrophic Growth Factor Val66Met Variation on Hippocampus Morphology in Bipolar Disorder
Chepenik LG, Fredericks C, Papademetris X, Spencer L, Lacadie C, Wang F, Pittman B, Duncan JS, Staib LH, Duman RS, Gelernter J, Blumberg HP. Effects of the Brain-Derived Neurotrophic Growth Factor Val66Met Variation on Hippocampus Morphology in Bipolar Disorder. Neuropsychopharmacology 2008, 34: 944-951. PMID: 18704093, PMCID: PMC2837582, DOI: 10.1038/npp.2008.107.Peer-Reviewed Original ResearchConceptsSmaller hippocampus volumesHippocampus volumeBipolar disorderBDNF genotypeBD diagnosisMood disorder pathophysiologyBDNF Val66Met polymorphismHigh-resolution magnetic resonanceHealthy comparison subjectsVal/Val homozygotesEffect of diagnosisLinear mixed model analysisVal66Met polymorphismGrowth factor proteinBD subgroupsDisorder pathophysiologyHC subjectsHippocampal developmentComparison subjectsMixed model analysisHippocampus structureBDNFHippocampus morphologyAnterior hippocampusVal homozygotes