2009
A 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 variabilityA 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. Lecture Notes In Computer Science 2009, 5761: 206-213. PMID: 20054422, PMCID: PMC2801876, DOI: 10.1007/978-3-642-04268-3_26.Peer-Reviewed Original ResearchSubject-specific dynamical modelCurrent frameMotion patternsRecursive Bayesian frameworkSegmentation taskPast framesAutomatic segmentationPrevious frameSegmentation processShape priorsLV segmentationManual segmentationSegmentationIntensity informationCardiac sequenceEchocardiographic sequencesStatic modelPrior knowledgeTemporal coherenceDynamical shape priorsCardiac motionCardiac modelsBayesian frameworkGeneric dynamical modelEchocardiographic images
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 modelImagesObjectsJoint 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 distribution