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. 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 imagesMine 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 model
2021
Shape-Regularized Unsupervised Left Ventricular Motion Network With Segmentation Capability In 3d+ Time Echocardiography
Ta K, Ahn SS, Stendahl JC, Sinusas AJ, Duncan JS. Shape-Regularized Unsupervised Left Ventricular Motion Network With Segmentation Capability In 3d+ Time Echocardiography. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2021, 00: 536-540. PMID: 34168721, PMCID: PMC8221369, DOI: 10.1109/isbi48211.2021.9433888.Peer-Reviewed Original ResearchConvolutional neural networkAccurate motion estimationCardiac motion patternsMotion estimation performanceDense displacement fieldB-mode echocardiography imagesSegmentation masksMedical imagesMotion estimationNeural networkSegmentation capabilityTarget imageUnsupervised estimationImportant taskSegmentationMotion patternsDisplacement fieldNetworkEchocardiography imagesEstimation performanceImagesLow signalAdditional challengesMotion networkNoise ratio
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
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 shapesInformationImagesJoint Prior Models of Neighboring Objects for 3D Image Segmentation
Yang J, Duncan JS. Joint Prior Models of Neighboring Objects for 3D Image Segmentation. 2015 IEEE Conference On Computer Vision And Pattern Recognition (CVPR) 2004, 1: i-314-i-319. PMID: 20448825, PMCID: PMC2864486, DOI: 10.1109/cvpr.2004.1315048.Peer-Reviewed Original ResearchImage segmentationMultiple objectsNeighboring objectsVariation of objectsShape prior modelPrior modelMedical imagesInput imageTraining imagesPosteriori estimation modelMultidimensional dataTraining phasePoint correspondencesSegmentationMap shapeReference objectPrior knowledgePrior informationLevel set functionDistance functionEstimation modelObjectsImagesDifficult objectsJoint probability distributionSegmentation 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