2022
Federated Transfer Learning for Low-Dose PET Denoising: A Pilot Study With Simulated Heterogeneous Data
Zhou B, Miao T, Mirian N, Chen X, Xie H, Feng Z, Guo X, Li X, Zhou S, Duncan J, Liu C. Federated Transfer Learning for Low-Dose PET Denoising: A Pilot Study With Simulated Heterogeneous Data. IEEE Transactions On Radiation And Plasma Medical Sciences 2022, 7: 284-295. PMID: 37789946, PMCID: PMC10544830, DOI: 10.1109/trpms.2022.3194408.Peer-Reviewed Original ResearchLow-dose PETMedical data privacy regulationsFederated learning algorithmLarge domain shiftTransfer learning frameworkData privacy regulationsHigh-quality reconstructionFederated transferData privacyHeterogeneous dataDomain shiftLearning frameworkLearning algorithmPrivacy regulationsData distributionCollaborative trainingLow-dose dataPET reconstructionPrevious methodsFL methodEfficient wayLocal dataSuperior performanceExperimental resultsDenoisingDual-domain self-supervised learning for accelerated non-Cartesian MRI reconstruction
Zhou B, Schlemper J, Dey N, Mohseni Salehi SS, Sheth K, Liu C, Duncan JS, Sofka M. Dual-domain self-supervised learning for accelerated non-Cartesian MRI reconstruction. Medical Image Analysis 2022, 81: 102538. PMID: 35926336, DOI: 10.1016/j.media.2022.102538.Peer-Reviewed Original ResearchConceptsNon-Cartesian MRI reconstructionMRI reconstructionUndersampled dataPrevious baseline methodsSelf-supervised approachSelf-supervised learningHigh-quality reconstructionReconstruction networkAppearance consistencyDataset demonstrateBaseline methodsImage domainDisjoint partitionsSupervised trainingPractical adoptionReconstruction accuracyDomain partitionImproved image qualityImage qualityDDSSSampling patternK-spaceExperimental resultsNetworkMotion robustness
2021
DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography
Zhou B, Chen X, Zhou SK, Duncan JS, Liu C. DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography. Medical Image Analysis 2021, 75: 102289. PMID: 34758443, PMCID: PMC8678361, DOI: 10.1016/j.media.2021.102289.Peer-Reviewed Original ResearchConceptsRecurrent networksSevere streak artifactsRecurrent frameworkArtifact reductionSparse viewsImage domainReconstruction qualityCT metal artifact reductionX-ray projectionsMetal artifact reductionArtifact-free imagesMedical diagnosisPrevious methodsProjection dataConsistent layerExperimental resultsMDPET: A Unified Motion Correction and Denoising Adversarial Network for Low-Dose Gated PET
Zhou B, Tsai YJ, Chen X, Duncan JS, Liu C. MDPET: A Unified Motion Correction and Denoising Adversarial Network for Low-Dose Gated PET. IEEE Transactions On Medical Imaging 2021, 40: 3154-3164. PMID: 33909561, PMCID: PMC8588635, DOI: 10.1109/tmi.2021.3076191.Peer-Reviewed Original ResearchConceptsMotion estimationPyramid networkAdversarial networkAccurate motion estimationMotion correctionLow-noise reconstructionGated positron emission tomographyMotion correction methodMotion estimation networkGated PET dataEstimation networkRecurrent layersDenoising NetworkRespiratory motion blurringExperimental resultsLow-noise imagesMotion blurringNoise levelCorrection methodNetworkPET reconstructionPrevious methodsImage qualityImagesEstimationMulti-frame Attention Network for Left Ventricle Segmentation in 3D Echocardiography
Ahn SS, Ta K, Thorn S, Langdon J, Sinusas AJ, Duncan JS. Multi-frame Attention Network for Left Ventricle Segmentation in 3D Echocardiography. Lecture Notes In Computer Science 2021, 12901: 348-357. PMID: 34729554, PMCID: PMC8560213, DOI: 10.1007/978-3-030-87193-2_33.Peer-Reviewed Original ResearchPerformance of segmentationLeft ventricle segmentationVentricle segmentationMedical image segmentation modelsSpatiotemporal featuresAttention networkImage segmentation modelSequence of imagesAttention mechanismSegmentation modelTedious taskTarget imageSegmentationEchocardiography imagesExperimental resultsImagesNetworkLimited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer
Zhou B, Zhou S, Duncan JS, Liu C. Limited View Tomographic Reconstruction Using a Cascaded Residual Dense Spatial-Channel Attention Network With Projection Data Fidelity Layer. IEEE Transactions On Medical Imaging 2021, 40: 1792-1804. PMID: 33729929, PMCID: PMC8325575, DOI: 10.1109/tmi.2021.3066318.Peer-Reviewed Original ResearchConceptsAttention networkView reconstructionGrand challenge datasetLimited angle reconstructionHigh-quality reconstructionNeural network methodSparse-view reconstructionExperimental resultsLimited angle acquisitionArchitecture issuesSparse viewsChallenge datasetLimited view dataView dataNeural architectureQuality reconstructionNetwork methodTomographic reconstructionReconstructed imagesProjection viewsPrevious methodsAngle reconstructionDatasetNetworkAngle acquisitionAnatomy-guided multimodal registration by learning segmentation without ground truth: Application to intraprocedural CBCT/MR liver segmentation and registration
Zhou B, Augenfeld Z, Chapiro J, Zhou SK, Liu C, Duncan JS. Anatomy-guided multimodal registration by learning segmentation without ground truth: Application to intraprocedural CBCT/MR liver segmentation and registration. Medical Image Analysis 2021, 71: 102041. PMID: 33823397, PMCID: PMC8184611, DOI: 10.1016/j.media.2021.102041.Peer-Reviewed Original ResearchConceptsMultimodal registrationLiver segmentationLarge-scale manual annotationGround truthMultimodal image registrationMultimodal registration methodSegmentation networkDomain adaptationManual annotationSource modalityImage registrationRegistration frameworkSegmentationImage-guided interventionsRegistration methodMedical imagingDiagnostic medical imagingCorrect transformationLimited FOVStructure informationIntraprocedural CBCTImage qualitySegmenterExperimental resultsPatient data
2010
Tracking 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 Research
2009
Segmentation of the Left Ventricle from Cardiac MR Images Using a Subject-Specific Dynamical Model
Zhu Y, Papademetris X, Sinusas AJ, Duncan JS. Segmentation of the Left Ventricle from Cardiac MR Images Using a Subject-Specific Dynamical Model. IEEE Transactions On Medical Imaging 2009, 29: 669-687. PMID: 19789107, PMCID: PMC2832728, DOI: 10.1109/tmi.2009.2031063.Peer-Reviewed Original ResearchConceptsSubject-specific dynamical modelGeneric dynamical modelDynamical modelStatistical modelSpecific dynamical modelRecursive Bayesian frameworkDynamic prediction algorithmStatic modelBayesian frameworkCardiac sequenceMotion modelActive Appearance Motion ModelsError propagationSpecific motion patternsPeriodic natureExperimental resultsPropagationCardiac shapeSegmentation resultsBackward directionSequential segmentationDynamicsModelMotion patternsOne-out
2008
Segmentation of Left Ventricle from 3D Cardiac MR Image Sequences Using a Subject-Specific Dynamical Model
Zhu Y, Papademetris X, Sinusas A, Duncan JS. Segmentation of Left Ventricle from 3D Cardiac MR Image Sequences Using a Subject-Specific Dynamical Model. 2015 IEEE Conference On Computer Vision And Pattern Recognition (CVPR) 2008, 2008: 1-8. PMID: 20052308, PMCID: PMC2801445, DOI: 10.1109/cvpr.2008.4587433.Peer-Reviewed Original ResearchSubject-specific dynamical modelGeneric dynamical modelDynamical modelSpecific dynamical modelRecursive Bayesian frameworkStatic modelBayesian frameworkStatistical modelCardiac sequenceCardiac MR image sequencesModel-based segmentationSpecific motion patternsCardiac shapeMR image sequencesImage sequencesMotion patternsModelOne-outLocal consistencyCurrent frameExperimental resultsSegmentation resultsDynamicsPast framesInter-subject variability
2003
Combinative multi-scale level set framework for echocardiographic image segmentation
Lin N, Yu W, Duncan JS. Combinative multi-scale level set framework for echocardiographic image segmentation. Medical Image Analysis 2003, 7: 529-537. PMID: 14561556, DOI: 10.1016/s1361-8415(03)00035-5.Peer-Reviewed Original ResearchConceptsLevel set frameworkShape knowledgeTedious human effortsSet frameworkEchocardiographic image sequencesLine training processEchocardiographic image segmentationUltrasound imagesImage segmentationAutomatic segmentationHuman effortImage sequencesBoundary detectionCoarse boundariesEdge featuresTraining processShape templatePoor featuresEndocardial boundarySegmentationContour evolutionRegion homogeneityImagesExperimental resultsCoarse scale