2025
The implementation of artificial intelligence in serial monitoring of post gamma knife vestibular schwannomas: A pilot study
Singh M, Jester N, Lorr S, Briano A, Schwartz N, Mahajan A, Chiang V, Tommasini S, Wiznia D, Buono F. The implementation of artificial intelligence in serial monitoring of post gamma knife vestibular schwannomas: A pilot study. Clinical Imaging 2025, 123: 110495. PMID: 40388858, DOI: 10.1016/j.clinimag.2025.110495.Peer-Reviewed Original ResearchConceptsGamma knife radiosurgeryVestibular schwannomaPost-GKSTumor growthVS volumeHalting tumor growthRetrospective cohort studyAssess treatment efficacyManual segmentationHearing lossBenign tumorsPaired t-testT1-weighted MRI scansAssess statistical significanceCohort studyDice similarity coefficientNeurological functionTreatment efficacyClinical monitoringMRI scansStatistical significancePercentage changeSchwannomaSpatial overlapTumorThe development of an artificial intelligence auto-segmentation tool for 3D volumetric analysis of vestibular schwannomas
Jester N, Singh M, Lorr S, Tommasini S, Wiznia D, Buono F. The development of an artificial intelligence auto-segmentation tool for 3D volumetric analysis of vestibular schwannomas. Scientific Reports 2025, 15: 5918. PMID: 39966622, PMCID: PMC11836447, DOI: 10.1038/s41598-025-88589-x.Peer-Reviewed Original ResearchConceptsGround-truth datasetDice scoreVestibular schwannomaImage processing accuracyVolumetric analysisML-based algorithmsMeasuring tumor sizeMean dice scoreAuto-segmentation toolAccurate AIAI modelsTumor sizeTumor modelVS tumorsTumor growthTesting stageAI-LTumorImage processing softwareClinical practicePatient recruitmentProcessing softwareSchwannomaDatasetManual segmentation
2024
Portable, low-field magnetic resonance imaging for evaluation of Alzheimer’s disease
Sorby-Adams A, Guo J, Laso P, Kirsch J, Zabinska J, Garcia Guarniz A, Schaefer P, Payabvash S, de Havenon A, Rosen M, Sheth K, Gomez-Isla T, Iglesias J, Kimberly W. Portable, low-field magnetic resonance imaging for evaluation of Alzheimer’s disease. Nature Communications 2024, 15: 10488. PMID: 39622805, PMCID: PMC11612292, DOI: 10.1038/s41467-024-54972-x.Peer-Reviewed Original ResearchConceptsWhite matter hyperintensitiesMachine learning pipelineMild cognitive impairmentAlzheimer's diseaseWhite matter hyperintensities volumeLearning pipelineAssessment of patientsIncrease accessCognitive impairmentEvaluation of Alzheimer's diseaseDementiaLF-MRIPoint-of-care assessmentMagnetic resonance imagingHippocampal volumeResonance imagingImage qualityDiseaseReduce costsAnisotropic counterpartIncreasing availabilityManual segmentationAutomated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics
Gross M, Huber S, Arora S, Ze’evi T, Haider S, Kucukkaya A, Iseke S, Kuhn T, Gebauer B, Michallek F, Dewey M, Vilgrain V, Sartoris R, Ronot M, Jaffe A, Strazzabosco M, Chapiro J, Onofrey J. Automated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics. European Radiology 2024, 34: 5056-5065. PMID: 38217704, PMCID: PMC11245591, DOI: 10.1007/s00330-023-10495-5.Peer-Reviewed Original ResearchMagnetic resonance imagingRadiomics feature extractionLiver volumetryIntraclass correlation coefficientRadiomic featuresLiver segmentationAutomated liver volumetryHepatocellular carcinoma patientsMann-Whitney U testAutomated liver segmentationManual segmentationQuantitative imaging biomarkersCarcinoma patientsRetrospective studyInstitutional databaseAnatomical localizationClinical relevanceManual volumetryMann-WhitneyU testExternal validationInternal test setImaging biomarkersInclusion criteriaResultsIn total
2023
Convolutional Neural Network for Fully Automated Cerebellar Volumetry in Children in Comparison to Manual Segmentation and Developmental Trajectory of Cerebellar Volumes
Sobootian D, Bronzlik P, Spineli L, Becker L, Winther H, Bueltmann E. Convolutional Neural Network for Fully Automated Cerebellar Volumetry in Children in Comparison to Manual Segmentation and Developmental Trajectory of Cerebellar Volumes. The Cerebellum 2023, 23: 1074-1085. PMID: 37833550, PMCID: PMC11102395, DOI: 10.1007/s12311-023-01609-2.Peer-Reviewed Original ResearchConceptsCerebellar volumeConvolutional neural networkCerebellum of childrenPediatric MRI examinationsAge-related volume changesT1-weighted magnetizationCerebellar volume measurementsNeural networkManual segmentationCerebellar volumetryComparison to manual segmentationSpearman correlation coefficientMRI examinationsSignal abnormalitiesConventional MRIITK-SNAPCerebellar volume changesClinical useDeep learning algorithmsVolume measurementsVolumetryCerebellumLearning algorithmsGradient echoTrained NNLiverHccSeg: A publicly available multiphasic MRI dataset with liver and HCC tumor segmentations and inter-rater agreement analysis
Gross M, Arora S, Huber S, Kücükkaya A, Onofrey J. LiverHccSeg: A publicly available multiphasic MRI dataset with liver and HCC tumor segmentations and inter-rater agreement analysis. Data In Brief 2023, 51: 109662. PMID: 37869619, PMCID: PMC10587725, DOI: 10.1016/j.dib.2023.109662.Peer-Reviewed Original ResearchTumor segmentation algorithmTumor segmentationSegmentation algorithmLiver segmentationManual segmentationTumor segmentation taskHigh-quality segmentationSegmentation taskSegmentation metricsSegmentation performanceAccurate segmentationRelevant metadataSegmentation agreementSegmentationMedical imagingFeature analysisExternal dataDatasetIntra-rater variabilityAlgorithmInnovative solutionsAutomated and manual segmentation of the hippocampus in human infants
Fel J, Ellis C, Turk-Browne N. Automated and manual segmentation of the hippocampus in human infants. Developmental Cognitive Neuroscience 2023, 60: 101203. PMID: 36791555, PMCID: PMC9957787, DOI: 10.1016/j.dcn.2023.101203.Peer-Reviewed Original ResearchConceptsManual segmentationHippocampal segmentationInter-rater reliabilityAutomated SegmentationSegmentation methodSegmentationAnatomical MRI scansEarly hippocampal developmentHead motionFreeSurfer softwareSoftwareHippocampal developmentMRI scansHippocampal structureAwake infantsInfantsHippocampusFMRI protocolAnatomical scansGold standardAdult templateHuman infantsScan qualityScansProtocol
2022
Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction
Aboian M, Bousabarah K, Kazarian E, Zeevi T, Holler W, Merkaj S, Petersen G, Bahar R, Subramanian H, Sunku P, Schrickel E, Bhawnani J, Zawalich M, Mahajan A, Malhotra A, Payabvash S, Tocino I, Lin M, Westerhoff M. Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction. Frontiers In Neuroscience 2022, 16: 860208. PMID: 36312024, PMCID: PMC9606757, DOI: 10.3389/fnins.2022.860208.Peer-Reviewed Original ResearchBrain tumor segmentationMedical imagesFeature extractionTumor segmentationRadiomic feature extractionDiagnostic workstationDeep learning-based algorithmPatient's medical imagesLearning-based algorithmFeature extraction toolImage processing algorithmsYale New Haven HealthGround truth dataImage annotationAI-segmentationAI algorithmsArtificial intelligenceEnd workflowProcessing algorithmsPicture archivingLarge datasetsLarge expertManual modificationInternal datasetManual segmentation
2021
Anatomy-Constrained Contrastive Learning for Synthetic Segmentation Without Ground-Truth
Zhou B, Liu C, Duncan J. Anatomy-Constrained Contrastive Learning for Synthetic Segmentation Without Ground-Truth. Lecture Notes In Computer Science 2021, 12901: 47-56. DOI: 10.1007/978-3-030-87193-2_5.Peer-Reviewed Original ResearchSegmentation networkContrastive learningManual segmentationSuperior segmentation performanceObject of interestSynthetic SegmentationManual effortSegmentation performanceTraining dataUnsupervised adaptationImaging dataSource modalitySegmentationNetworkPrevious methodsLearningLarge amountSuccessful applicationPET imaging dataImagesObjectsCodeDataNew imaging modality
2020
Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI
McGrath H, Li P, Dorent R, Bradford R, Saeed S, Bisdas S, Ourselin S, Shapey J, Vercauteren T. Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI. International Journal Of Computer Assisted Radiology And Surgery 2020, 15: 1445-1455. PMID: 32676869, PMCID: PMC7419453, DOI: 10.1007/s11548-020-02222-y.Peer-Reviewed Original ResearchConceptsManual segmentationHigh quality softwareTime-intensive taskGeneric softwareIntensive tasksSegmentation accuracySegmentation timeSegmentation approachVestibular schwannomaSegmentationSegmentation effortsApplicable solutionSoftwareReference approachCurrent clinical practiceVestibular schwannoma volumeAccuracyContrast agent injectionVolumetric measurementsEqual performanceTumor sizeVS sizeClinical practiceMore frustrationAgent injectionAutomated 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 MRIThresholding
2019
Atlas-Based Multiorgan Segmentation for Dynamic Abdominal PET
Ren S, Laub P, Lu Y, Naganawa M, Carson R. Atlas-Based Multiorgan Segmentation for Dynamic Abdominal PET. IEEE Transactions On Radiation And Plasma Medical Sciences 2019, 4: 50-62. DOI: 10.1109/trpms.2019.2926889.Peer-Reviewed Original ResearchMultiorgan segmentationSegmentation methodDynamic image seriesDynamic imagesFrames of dataHyperparameter optimizationAutomatic frameworkSegmentation frameworkDynamic PET imagesSegmentation resultsGraph cutsFinal segmentationHyperparameter combinationsTemporal informationAtlas informationPET imagesTissue time-activity curvesManual segmentationSegmentationImage seriesGaussian distributionPrincipal component analysisBetter performanceImagesDifferent activity distributions
2017
Fully automatic multi-atlas segmentation of CTA for partial volume correction in cardiac SPECT/CT
Liu Q, Mohy-ud-Din H, Boutagy N, Jiang M, Ren S, Stendahl J, Sinusas A, Liu C. Fully automatic multi-atlas segmentation of CTA for partial volume correction in cardiac SPECT/CT. Physics In Medicine And Biology 2017, 62: 3944-3957. PMID: 28266929, PMCID: PMC5568763, DOI: 10.1088/1361-6560/aa6520.Peer-Reviewed Original ResearchConceptsMulti-atlas segmentation methodLabel fusion methodMulti-atlas segmentationSegmentation methodConventional label fusion methodsFusion methodManual segmentationMultiple organ segmentationLabel fusion algorithmImage qualityCardiac SPECT imagesDice similarity coefficientOrgan segmentationSegmentation accuracyAutomatic segmentationCTA segmentationFusion algorithmComputed tomography angiography dataSegmentationOne-out approachCT datasetsTomography angiography dataSimilarity coefficientAngiography dataConsistent image quality
2014
Volumetric brain analysis in neurosurgery: Part 1. Particle filter segmentation of brain and cerebrospinal fluid growth dynamics from MRI and CT images.
Mandell J, Langelaan J, Webb A, Schiff S. Volumetric brain analysis in neurosurgery: Part 1. Particle filter segmentation of brain and cerebrospinal fluid growth dynamics from MRI and CT images. Journal Of Neurosurgery Pediatrics 2014, 15: 113-24. PMID: 25431902, DOI: 10.3171/2014.9.peds12426.Peer-Reviewed Original ResearchConceptsEdge trackerParticle filterGround vehicle navigationBrain image analysisCT imagesMRI data setsImage segmentationSegmentation algorithmAutonomous airVehicle navigationAccurate edgesNovel algorithmManual segmentationSegmentationMR imagesBrain dataVolumetric brain analysisData setsImage analysisSemiautomatic methodImagesModality independenceHistorical dataAlgorithmMRI data
2010
A coupled deformable model for tracking myocardial borders from real-time echocardiography using an incompressibility constraint
Zhu Y, Papademetris X, Sinusas AJ, Duncan JS. A coupled deformable model for tracking myocardial borders from real-time echocardiography using an incompressibility constraint. Medical Image Analysis 2010, 14: 429-448. PMID: 20350833, PMCID: PMC4318707, DOI: 10.1016/j.media.2010.02.005.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsArtificial IntelligenceComputer SystemsDogsEchocardiography, Three-DimensionalElasticity Imaging TechniquesHumansImage EnhancementImage Interpretation, Computer-AssistedPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueConceptsDeformable modelImage-derived informationLV endocardial boundariesImage acquisition techniquesFinal segmentationAutomatic algorithmGround truthManual segmentationVolumetric imagesSegmentationSynthetic dataEndocardial boundaryNumber of effortsMyocardial bordersEpicardial boundariesAcquisition techniquesInstantaneous acquisitionConstraintsImagesEchocardiographic imagesSetSpeckle statisticsAlgorithmReal-time echocardiographyIntegrated 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
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. 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 imagesA 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 variability
2007
Segmentation of Myocardial Volumes from Real-Time 3D Echocardiography Using an Incompressibility Constraint
Zhu Y, Papademetris X, Sinusas A, Duncan JS. Segmentation of Myocardial Volumes from Real-Time 3D Echocardiography Using an Incompressibility Constraint. Lecture Notes In Computer Science 2007, 10: 44-51. PMID: 18051042, DOI: 10.1007/978-3-540-75757-3_6.Peer-Reviewed Original ResearchConceptsAutomatic segmentationImage-derived informationLV endocardial boundariesFinal representationManual segmentationSegmentationEndocardial boundaryEpicardial boundariesReal-time 3D echocardiographyTight couplingNew approachThree-dimensional shapeConstraintsVariety of effortsRepresentationInformationNew imaging modalitySet
1999
Segmentation 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
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