2022
Learning Correspondences of Cardiac Motion from Images Using Biomechanics-Informed Modeling
Zhang X, You C, Ahn S, Zhuang J, Staib L, Duncan J. Learning Correspondences of Cardiac Motion from Images Using Biomechanics-Informed Modeling. Lecture Notes In Computer Science 2022, 13593: 13-25. DOI: 10.1007/978-3-031-23443-9_2.Peer-Reviewed Original ResearchDisplacement vector fieldQuantitative evaluation metricsCardiac anatomical structuresTraining complexityExtensive experimentsSegmentation performanceBiomechanical feasibilityEvaluation metricsAvailable datasetsCardiac motionSmoothness constraintGeometric constraintsBiomechanical propertiesDatasetMRI dataPlausible transformationsMotionConstraintsAnatomical structuresRegularizerRegularization schemeMethodIncompressibilityImagesMetricsIdentifying 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
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
2000
PathMaster
Mattie M, Staib L, Stratmann E, Tagare H, Duncan J, Miller P. PathMaster. Journal Of The American Medical Informatics Association 2000, 7: 404-415. PMID: 10887168, PMCID: PMC61444, DOI: 10.1136/jamia.2000.0070404.Peer-Reviewed Original ResearchConceptsDigital image databaseText-based descriptionsFeature extraction routineImage databaseIndexing methodSearch enginesFeature extractionCytopathology imagesCross-reference analysisExtraction routinesImagesPrognostic processInformation contentDescriptorsIndividual cell characteristicsDatabaseCell descriptorsRoutinesRecognition trialsEngineIndex dataExtractionMethodDescription
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 anatomyInformationConstraintsMethod