2023
Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning
Kucukkaya A, Zeevi T, Chai N, Raju R, Haider S, Elbanan M, Petukhova-Greenstein A, Lin M, Onofrey J, Nowak M, Cooper K, Thomas E, Santana J, Gebauer B, Mulligan D, Staib L, Batra R, Chapiro J. Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning. Scientific Reports 2023, 13: 7579. PMID: 37165035, PMCID: PMC10172370, DOI: 10.1038/s41598-023-34439-7.Peer-Reviewed Original Research
2022
Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities
Merkaj S, Bahar RC, Zeevi T, Lin M, Ikuta I, Bousabarah K, Petersen G, Staib L, Payabvash S, Mongan JT, Cha S, Aboian MS. Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities. Cancers 2022, 14: 2623. PMID: 35681603, PMCID: PMC9179416, DOI: 10.3390/cancers14112623.Peer-Reviewed Original ResearchMachine learning toolsGrade predictionLearning toolsML applicationsClassifier algorithmML modelsClassification methodMedical imagingData sourcesPractices of radiologistsToolGlioma gradingNext stepWorkflowAlgorithmChallengesTechnological innovationImplementationPredictionModelLast decadeSpecific areasMachine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment
Petersen G, Shatalov J, Verma T, Brim WR, Subramanian H, Brackett A, Bahar RC, Merkaj S, Zeevi T, Staib LH, Cui J, Omuro A, Bronen RA, Malhotra A, Aboian MS. Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment. American Journal Of Neuroradiology 2022, 43: 526-533. PMID: 35361577, PMCID: PMC8993193, DOI: 10.3174/ajnr.a7473.Peer-Reviewed Original ResearchConceptsMachine learning-based methodsLearning-based methodsBalanced data setData setsVector machine modelMachine learningClassification algorithmsMachine modelMachineAlgorithmData basesPrediction modelPromising resultsPrimary CNS lymphomaPrediction model study RiskRisk of biasRadiomic featuresClassifierSetCNS lymphomaWebLearningFeaturesQualitySystematic reviewIdentifying 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
2011
Unified Framework for Development, Deployment and Robust Testing of Neuroimaging Algorithms
Joshi A, Scheinost D, Okuda H, Belhachemi D, Murphy I, Staib LH, Papademetris X. Unified Framework for Development, Deployment and Robust Testing of Neuroimaging Algorithms. Neuroinformatics 2011, 9: 69-84. PMID: 21249532, PMCID: PMC3066099, DOI: 10.1007/s12021-010-9092-8.Peer-Reviewed Original ResearchConceptsUser interface controlsUser interfaceNovel object-oriented frameworkCommand-line user interfaceGraphical user interface controlsMedical image analysisObject-oriented frameworkComplex image analysisImage analysisPlatform interoperabilitySoftware objectsReusable componentsInterface controlSource codeSuch algorithmsFramework idealMultiple platformsUnified frameworkAlgorithmRapid developmentDeploymentThorough testingPublic useFrameworkPlatform
2009
Unified framework for development, deployment and testing of image analysis algorithms
Joshi A, Scheinost D, Okuda H, Murphy I, Staib L, Papademetris X. Unified framework for development, deployment and testing of image analysis algorithms. The MIDAS Journal 2009 DOI: 10.54294/pq6gf6.Peer-Reviewed Original ResearchImage analysis algorithmsUser interface controlsUser interfaceAnalysis algorithmCommand-line user interfaceGraphical user interface controlsPlatform interoperabilityInterface controlSource codeComplex algorithmsSuch algorithmsNovel frameworkFramework idealMultiple platformsUnified frameworkAlgorithmRapid developmentDeploymentCustom pipelineImage analysisUsersPublic useFrameworkInteroperabilityDevelopers
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
Correcting Nonuniformities in MRI Intensities Using Entropy Minimization Based on an Elastic Model
Bansal R, Staib L, Peterson B. Correcting Nonuniformities in MRI Intensities Using Entropy Minimization Based on an Elastic Model. Lecture Notes In Computer Science 2004, 3216: 78-86. DOI: 10.1007/978-3-540-30135-6_10.Peer-Reviewed Original ResearchPartial differential equationsConstraints of interestEntropy minimizationBody forceBias fieldDifferential equationsObserved imagesMathematical formulationOverall entropyElastic deformationEntropyElastic modelHomogeneous regionsMinimizationFieldConstraintsFormulationEquationsNonuniformityMultiplicative bias fieldAlgorithmOriginal imageDeformationForce
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 verificationComputing 3D Non-rigid Brain Registration Using Extended Robust Point Matching for Composite Multisubject fMRI Analysis
Papademetris X, Jackowski A, Schultz R, Staib L, Duncan J. Computing 3D Non-rigid Brain Registration Using Extended Robust Point Matching for Composite Multisubject fMRI Analysis. Lecture Notes In Computer Science 2003, 2879: 788-795. DOI: 10.1007/978-3-540-39903-2_96.Peer-Reviewed Original ResearchRobust Point MatchingIntensity-based registrationLarge point setsPoint matchingGreater computational efficiencyComputational efficiencyRobust pointSuperior performancePoint setsMagnetic resonance imagesBrain registrationActivation mapsFunctional magnetic resonance imagesSuccessful applicationRegistrationResonance imagesAlgorithmMatchingSpecificationMethodologyImagesRobustnessFrameworkSpecific areasSet
1999
Entropy-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 estimationInitialization
1998
A novel approach for the registration of 2D portal and 3D CT images for treatment setup verification in radiotherapy
Bansal R, Staib L, Chen Z, Rangarajan A, Knisely J, Nath R, Duncan J. A novel approach for the registration of 2D portal and 3D CT images for treatment setup verification in radiotherapy. Lecture Notes In Computer Science 1998, 1496: 1075-1086. DOI: 10.1007/bfb0056297.Peer-Reviewed Original ResearchRegistration parametersMulti-modality image registrationResolution imagesPortal imagesLow-resolution imagesMutual information metricHigh-resolution imagesImage registrationCT data setsSetup verificationLow contrastInformation metricData setsDifficult problemLow resolutionNovel approachImagesAlgorithmCT dataRegistrationCT imagesVerificationEntropy algorithmSegmentionMetrics
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