2024
Prior knowledge-guided vision-transformer-based unsupervised domain adaptation for intubation prediction in lung disease at one week
Yang J, Henao J, Dvornek N, He J, Bower D, Depotter A, Bajercius H, de Mortanges A, You C, Gange C, Ledda R, Silva M, Dela Cruz C, Hautz W, Bonel H, Reyes M, Staib L, Poellinger A, Duncan J. Prior knowledge-guided vision-transformer-based unsupervised domain adaptation for intubation prediction in lung disease at one week. Computerized Medical Imaging And Graphics 2024, 118: 102442. PMID: 39515190, DOI: 10.1016/j.compmedimag.2024.102442.Peer-Reviewed Original ResearchUnsupervised domain adaptationSpatial prior informationDomain adaptationLabeled dataData-driven approachUnsupervised domain adaptation modelMedical image analysis tasksImage analysis tasksTransformer-based modelsMedical image analysisPrior informationOutcome prediction tasksAdversarial trainingDistribution alignmentDomain shiftAttention headsClass tokenPoor generalizationAnalysis tasksTarget domainPrediction taskData distributionKnowledge-guidedLocal weightsMedical imagesHeteroscedastic Uncertainty Estimation Framework for Unsupervised Registration
Zhang X, Pak D, Ahn S, Li X, You C, Staib L, Sinusas A, Wong A, Duncan J. Heteroscedastic Uncertainty Estimation Framework for Unsupervised Registration. Lecture Notes In Computer Science 2024, 15002: 651-661. DOI: 10.1007/978-3-031-72069-7_61.Peer-Reviewed Original ResearchUnsupervised registrationReal-world medical imagesCollaborative training strategyMedical image datasetsDeep learning methodsAccurate displacement estimationSignal-to-noise ratioImage datasetsRegistration architectureLearning methodsMedical imagesTraining strategyNoise distributionUncertainty estimationWeighting schemeRegistration performanceSpatial domainEstimation frameworkInput-dependentUncertainty estimation frameworkUniform noise levelsDisplacement estimationFrameworkNoise levelUnsupervisedAdaptive Correspondence Scoring for Unsupervised Medical Image Registration
Zhang X, Stendahl J, Staib L, Sinusas A, Wong A, Duncan J. Adaptive Correspondence Scoring for Unsupervised Medical Image Registration. Lecture Notes In Computer Science 2024, 15096: 76-92. DOI: 10.1007/978-3-031-72920-1_5.Peer-Reviewed Original ResearchMedical image registrationAdaptation frameworkMedical image datasetsUnsupervised learning schemeAdaptive training schemeImage registrationError residualsSupervision signalsLearning schemeImage datasetsRegistration architectureIntensity constancyScore mapNoisy gradientsMedical imagesTraining schemeImage reconstructionPerformance degradationLambertian assumptionCorrespondence scoresLoss of correspondenceTraining objectivesDisplacement estimationImage acquisitionSchemeMine yOur owN Anatomy: Revisiting Medical Image Segmentation With Extremely Limited Labels
You C, Dai W, Liu F, Min Y, Dvornek N, Li X, Clifton D, Staib L, Duncan J. Mine yOur owN Anatomy: Revisiting Medical Image Segmentation With Extremely Limited Labels. IEEE Transactions On Pattern Analysis And Machine Intelligence 2024, 46: 11136-11151. PMID: 39269798, DOI: 10.1109/tpami.2024.3461321.Peer-Reviewed Original ResearchMedical image segmentationImage segmentationMedical image segmentation frameworkContext of medical image segmentationLong-tailed class distributionStrong data augmentationsIntra-class variationsSemi-supervised settingData imbalance issueImage segmentation frameworkMedical image analysisMedical image dataSupervision signalsContrastive learningBenchmark datasetsUnsupervised mannerLabel setsData augmentationSegmentation frameworkDomain expertisePseudo-codeImbalance issueModel trainingMedical imagesSegmentation modelChapter 20 Motion and deformation recovery and analysis
Duncan J, Staib L. Chapter 20 Motion and deformation recovery and analysis. 2024, 519-548. DOI: 10.1016/b978-0-12-813657-7.00033-9.Peer-Reviewed Original ResearchArtificial neural networkData-driven approachDeep learningMedical imagesOptical flow methodMotion estimationNeural networkDense flow fieldLocal informationShape trackingFeature trackingAlgorithm performanceMedical imagingExample applicationUse of modelsDeformation recoveryFlow fieldTrackingDeformation analysisKalman filteringMotion analysisBiomechanical deformationFlow methodDeformationEvaluation approach
2009
From 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 ResearchConceptsResearch 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 techniques
2004
Neighbor-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 shapesInformationImagesSegmentation of 3D Deformable Objects with Level Set Based Prior Models
Yang J, Tagare HD, Staib LH, Duncan JS. Segmentation of 3D Deformable Objects with Level Set Based Prior Models. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2004, 1: 85-88. PMID: 20300448, PMCID: PMC2840654, DOI: 10.1109/isbi.2004.1398480.Peer-Reviewed Original ResearchMultiple objectsMedical imagesObject shapeExplicit point correspondencesShape prior constraintVariation of objectsTraining imagesMultidimensional dataTraining phaseDeformable modelDeformable objectsPoint correspondencesSegmentationPrior constraintsPrior informationLevel set functionPrior modelEstimation modelImagesObjectsLevel setsSet functionMaximum ARepresentationPoint distribution
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
1996
Parameterized Feasible Boundaries in Gradient Vector Fields
Worring M, Smeulders A, Staib L, Duncan J. Parameterized Feasible Boundaries in Gradient Vector Fields. Computer Vision And Image Understanding 1996, 63: 135-144. DOI: 10.1006/cviu.1996.0009.Peer-Reviewed Original ResearchImage informationModel-based segmentation procedureObject boundariesSegmentation of imagesDirectional gradient informationLocal image informationObjective functionReal medical imagesObject of interestObject boundary extractionMedical imagesImage dataSmoothness objectiveConflicting objectsBoundary extractionComplex imagesGradient informationArtificial dataSegmentation procedureFeasible boundaryGradient magnitudeSegmentationPhysical feasibilityImagesObjectsModel-based deformable surface finding for medical images
Staib L, Duncan J. Model-based deformable surface finding for medical images. IEEE Transactions On Medical Imaging 1996, 15: 720-731. PMID: 18215953, DOI: 10.1109/42.538949.Peer-Reviewed Original ResearchDeformable boundary finding in medical images by integrating gradient and region information
Chakraborty A, Staib L, Duncan J. Deformable boundary finding in medical images by integrating gradient and region information. IEEE Transactions On Medical Imaging 1996, 15: 859-870. PMID: 18215965, DOI: 10.1109/42.544503.Peer-Reviewed Original ResearchBoundary findingMedical imagesHomogeneous region-classified areaBiomedical image analysisGray level homogeneityRegion-based segmentationReal medical imagesComputational overheadImage segmentationRegion informationShape informationPoor initializationPerceptual notionsImage analysisNumber of experimentsSegmentationVariety of limitationsGreen's theoremImagesUnified approachAuthors' approachKey issuesNew approachOverheadInformationAn integrated approach for surface finding in medical images
Chakraborty A, Staib L, Duncan J. An integrated approach for surface finding in medical images. 1996, 253-262. DOI: 10.1109/mmbia.1996.534077.Peer-Reviewed Original Research
1994
An integrated approach to boundary finding in medical images
Chakraborty A, Staib L, Duncan J. An integrated approach to boundary finding in medical images. 1994, 13-22. DOI: 10.1109/bia.1994.315870.Peer-Reviewed Original ResearchBoundary findingMedical imagesHomogeneous region-classified areaBiomedical image analysisGray level homogeneityReal medical imagesImage segmentationShape informationPoor initializationPerceptual notionsImage analysisNumber of experimentsSegmentationVariety of limitationsConventional gradientImagesUnified approachAuthors' approachKey issuesNew approachGreen's theoremConventional methodsIntegrated approachInitializationFinder
1993
Parameterized feasible boundaries in gradient vector fields
Worring M, Smeulders A, Staib L, Duncan J. Parameterized feasible boundaries in gradient vector fields. Lecture Notes In Computer Science 1993, 687: 48-61. DOI: 10.1007/bfb0013780.Peer-Reviewed Original ResearchObject boundariesMedical imagesModel-based segmentation procedureComplex medical imagesSegmentation of imagesDirectional gradient informationLocal image informationReal medical imagesObject of interestImage informationImage dataSmoothness objectiveConflicting objectsNew objective functionProblem of extractionGradient informationArtificial dataSegmentation procedureFeasible boundarySegmentationPhysical feasibilityImagesObjective functionObjectsGradient vector field