2023
Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts
You C, Dai W, Min Y, Staib L, Duncan J. Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts. Lecture Notes In Computer Science 2023, 14222: 561-571. PMID: 38840671, PMCID: PMC11151725, DOI: 10.1007/978-3-031-43898-1_54.Peer-Reviewed Original ResearchMedical image segmentationImage segmentationSegmentation methodPixel-level featuresComputer graphics problemImplicit neural representationsGrid-based representationMedical segmentationRendering frameworkSegmentation predictionsEnd mannerCorrelated contentCompetitive performance improvementsGraphics problemsSegmentationPoint representationPerformance improvementRegular gridSuch informationRepresentationConvolution operatorsExpertsComplex signalsRenderingFeatures
2022
Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation
You C, Xiang J, Su K, Zhang X, Dong S, Onofrey J, Staib L, Duncan J. Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation. Lecture Notes In Computer Science 2022, 13573: 3-16. PMID: 37415747, PMCID: PMC10323962, DOI: 10.1007/978-3-031-18523-6_1.Peer-Reviewed Original ResearchIncremental learningMedical image segmentation tasksMulti-site datasetImage segmentation tasksMedical image segmentationProstate MRI SegmentationComputation resourcesMedical datasetsSegmentation taskImage segmentationSegmentation frameworkEmbedding featuresBenchmark datasetsMRI segmentationTraining dataTarget domainLearning approachPractical deploymentDomain-specific expertiseCompetitive performanceDatasetTraining schemePrior workSegmentationSingle modelAtlas-Based Semantic Segmentation of Prostate Zones
Zhang J, Venkataraman R, Staib L, Onofrey J. Atlas-Based Semantic Segmentation of Prostate Zones. Lecture Notes In Computer Science 2022, 13435: 570-579. PMID: 38084296, PMCID: PMC10711803, DOI: 10.1007/978-3-031-16443-9_55.Peer-Reviewed Original ResearchSegmentation resultsSemantic segmentation frameworkSemantic segmentation resultsDice similarity coefficient valuesSemantic segmentationInference stageSegmentation frameworkSegmentation performanceExternal test datasetTest datasetRegion of interestSegmentationAnatomical atlasHyperparametersSimilarity coefficient valuesAnatomical informationGitHubUsersDatasetProstate zonesCodeFrameworkInformationIdentifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries
Tillmanns N, Lum AE, Cassinelli G, Merkaj S, Verma T, Zeevi T, Staib L, Subramanian H, Bahar RC, Brim W, Lost J, Jekel L, Brackett A, Payabvash S, Ikuta I, Lin M, Bousabarah K, Johnson MH, Cui J, Malhotra A, Omuro A, Turowski B, Aboian MS. Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries. Neuro-Oncology Advances 2022, 4: vdac093. PMID: 36071926, PMCID: PMC9446682, DOI: 10.1093/noajnl/vdac093.Peer-Reviewed Original ResearchGlioma segmentationResearch algorithmSegmentation of gliomasHigh accuracy resultsML algorithmsApplicable machineAccuracy resultsTCIA datasetSegmentationAlgorithmMachinePatient dataSystematic literature reviewOverfittingData extractionDatasetBratDatabaseRecent advancesResearch literatureLimitationsExtractionCurrent research literatureMethod
2020
Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning
Bousabarah K, Letzen B, Tefera J, Savic L, Schobert I, Schlachter T, Staib LH, Kocher M, Chapiro J, Lin M. Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning. Abdominal Radiology 2020, 46: 216-225. PMID: 32500237, PMCID: PMC7714704, DOI: 10.1007/s00261-020-02604-5.Peer-Reviewed Original ResearchConceptsDeep convolutional neural networkAverage false positive rateDice similarity coefficientU-NetDeep learning algorithmsConvolutional neural networkTest setMean Dice similarity coefficientRandom forest classifierDCNN methodDCNN approachDeep learningNet architectureLearning algorithmNeural networkLiver segmentationManual 3D segmentationForest classifierGround truthManual segmentationFalse positive rateCorresponding segmentationSegmentationMultiphasic contrast-enhanced MRIThresholdingSparse 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
Generalizable Multi-Site Training and Testing Of Deep Neural Networks Using Image Normalization
Onofrey JA, Casetti-Dinescu DI, Lauritzen AD, Sarkar S, Venkataraman R, Fan RE, Sonn GA, Sprenkle PC, Staib LH, Papademetris X. Generalizable Multi-Site Training and Testing Of Deep Neural Networks Using Image Normalization. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2019, 00: 348-351. PMID: 32874427, PMCID: PMC7457546, DOI: 10.1109/isbi.2019.8759295.Peer-Reviewed Original ResearchDeep neural networksNeural networkDeep learning algorithmsProstate gland segmentationImage normalization methodGland segmentationLearning algorithmImage normalizationMulti-site dataIntensity normalization methodNormalization methodSingle-site dataAlgorithmNetworkPotential solutionsEquipment sourcesClinical adoptionSegmentationTrainingIntensity characteristicsRobustnessDataSite trainingMethodAdoption
2010
Integrated 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 system
2006
BioImage Suite: An integrated medical image analysis suite: An update.
Papademetris X, Jackowski MP, Rajeevan N, DiStasio M, Okuda H, Constable RT, Staib LH. BioImage Suite: An integrated medical image analysis suite: An update. Insight Journal 2006, 2006: 209. PMID: 25364771, PMCID: PMC4213804, DOI: 10.54294/2g80r4.Peer-Reviewed Original ResearchVisualization ToolkitInsight ToolkitUser-friendly user interfaceTcl scripting languageArea of segmentationAnalysis software suiteScripting languageUser interfaceImage processingBioImage SuiteSoftware suiteAdditional algorithmsAnalysis suiteBeta versionImage analysisToolkitSuiteSegmentationDownloadAlgorithmLanguageYaleRegistrationProcessingUpdate
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 distributionGeometric strategies for neuroanatomic analysis from MRI
Duncan JS, Papademetris X, Yang J, Jackowski M, Zeng X, Staib LH. Geometric strategies for neuroanatomic analysis from MRI. NeuroImage 2004, 23: s34-s45. PMID: 15501099, PMCID: PMC2832750, DOI: 10.1016/j.neuroimage.2004.07.027.Peer-Reviewed Original ResearchConceptsApplied mathematical approachWhite matter fiber tracksStatistical estimationMathematical approachFunction-structure analysisMagnetic resonance imagesEvolution strategyGeometric constraintsImage processingIntersubject registrationRich setGeometric strategyOngoing workData setsUse of levelsCommon spaceNeuroanatomic analysisSetRegistrationFiber tracksHuman brainResonance imagesInformationSegmentationEstimation
2003
Entropy-Based Dual-Portal-to-3-DCT Registration Incorporating Pixel Correlation
Bansal R, Staib LH, Chen Z, Rangarajan A, Knisely J, Nath R, Duncan JS. Entropy-Based Dual-Portal-to-3-DCT Registration Incorporating Pixel Correlation. IEEE Transactions On Medical Imaging 2003, 22: 29. PMID: 12703758, DOI: 10.1109/tmi.2002.806430.Peer-Reviewed Original ResearchConceptsRegistration frameworkImage dataMutual information-based registration algorithmRegistration parametersPortal imagesUltrasound image dataReal patient dataTomography image dataImage pixelsPixel correlationRegistration algorithmPatient setup verificationSegmentationPixel intensityMarkov random processInitial versionTransformation parametersAppropriate entropyImagesAlgorithmPatient dataFrameworkCT imagesLine processSetup verificationNeighbor-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
1999
A New Approach to 3D Sulcal Ribbon Finding from MR Images
Zeng X, Staib L, Schultz R, Tagare H, Win L, Duncan J. A New Approach to 3D Sulcal Ribbon Finding from MR Images. Lecture Notes In Computer Science 1999, 1679: 148-157. DOI: 10.1007/10704282_16.Peer-Reviewed Original ResearchGeneral segmentation methodsMR brain imagesDistance functionLittle manual interventionDeformable surface modelSegmentation workSegmentation methodManual interventionNew approachBrain imagesContour modelCortex segmentationDynamic programmingLevel setsNatural followImagesMR imagesControl problemSurface modelSegmentationQuantitative resultsProgrammingEntropy-Based, Multiple-Portal-to-3DCT Registration for Prostate Radiotherapy Using Iteratively Estimated Segmentation
Bansal R, Staib L, Chen Z, Rangarajan A, Knisely J, Nath R, Duncan J. Entropy-Based, Multiple-Portal-to-3DCT Registration for Prostate Radiotherapy Using Iteratively Estimated Segmentation. Lecture Notes In Computer Science 1999, 1679: 567-578. DOI: 10.1007/10704282_61.Peer-Reviewed Original ResearchPatient setup verificationPortal imagesReal patient dataSingle portal imagePose parametersCT data setsRegistration frameworkRegistration parametersSetup verificationDifferent initializationsAlgorithmMultiple portalsIterative fashionData setsTransformation parametersAppropriate entropyImagesCT dataPatient dataVerificationNoise conditionsFrameworkSegmentationAccurate estimationInitializationSegmentation and measurement of the cortex from 3-D MR images using coupled-surfaces propagation
Zeng X, Staib L, Schultz R, Duncan J. Segmentation and measurement of the cortex from 3-D MR images using coupled-surfaces propagation. IEEE Transactions On Medical Imaging 1999, 18: 927-937. PMID: 10628952, DOI: 10.1109/42.811276.Peer-Reviewed Original ResearchConceptsImage-derived informationEasy initializationAutomatic segmentationEfficient segmentationMR imagesChallenging problemFinal representationManual segmentationThree-dimensional MR imagesSegmentationComputational efficiencyOutermost thin layerSuch problemsImagesTight couplingNew approachConvoluted natureRepresentationGeometric measurementsInitializationImplementationSulcal foldsBrain anatomyInformationConstraints
1998
Segmentation and measurement of the cortex from 3D MR images
Zeng X, Staib L, Schultz R, Duncan J. Segmentation and measurement of the cortex from 3D MR images. Lecture Notes In Computer Science 1998, 1496: 519-530. DOI: 10.1007/bfb0056237.Peer-Reviewed Original ResearchReal 3D MR imagesImage-derived informationEasy initializationAutomatic segmentationEfficient segmentationMR imagesChallenging problemFinal representationManual segmentationSegmentationComputational efficiencyOutermost thin layerImagesTight couplingNew approachConvoluted natureRepresentationGeometric measurementsInitializationSurface propagationImplementationBrain anatomyInformationConstraintsMethodVolumetric layer segmentation using coupled surfaces propagation
Zeng X, Staib L, Schultz R, Duncan J. Volumetric layer segmentation using coupled surfaces propagation. 2015 IEEE Conference On Computer Vision And Pattern Recognition (CVPR) 1998, 708-715. DOI: 10.1109/cvpr.1998.698681.Peer-Reviewed Original ResearchMedical image analysisUseful image informationMagnetic resonance brain imagesImage-derived informationImage gradient informationLevel set implementationGray level valuesEasy initializationSegmentation problemImage informationAutomatic segmentationGradient informationSet implementationBrain imagesLayer segmentationComputational efficiencyNon-brain structuresLeft ventricle myocardiumImage analysisSegmentationInformationNew approachTest examplesSurface propagationVentricle myocardium
1997
An Integrated Approach for Locating Neuroanatomical Structure from MRI
Staib L, Chakraborty A, Duncan J. An Integrated Approach for Locating Neuroanatomical Structure from MRI. International Journal Of Pattern Recognition And Artificial Intelligence 1997, 11: 1247-1269. DOI: 10.1142/s0218001497000585.Peer-Reviewed Original ResearchHomogeneous region-classified areaPrior shape informationMR brain imagesMagnetic resonance imagesComputational overheadRegion informationShape informationBrain imagesSuch imagesExtra informationImproper initializationThree-dimensional imagesDeformable surfacesImagesExperimental resultsGauss divergence theoremWide availabilityInformationOverheadResonance imagesSegmentationIntegrated approachHigh-resolution magnetic resonance imagesAlgorithmInitialization