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 areas
2011
Integrated Parcellation and Normalization Using DTI Fasciculography
Ho HP, Wang F, Papademetris X, Blumberg HP, Staib LH. Integrated Parcellation and Normalization Using DTI Fasciculography. Lecture Notes In Computer Science 2011, 14: 33-41. PMID: 21995010, PMCID: PMC3701295, DOI: 10.1007/978-3-642-23629-7_5.Peer-Reviewed Original ResearchConceptsDiffusion magnetic resonance imagesExtensive human interventionCumulative tracking errorsInteractive speedHuman interventionOrientation informationImage noiseMagnetic resonance imagesTracking errorVirtual pathwaysNormalization methodImagesDiffusion imagesWhite matter fasciclesFiber trackingCross-subject statisticsResonance imagesNew techniqueTrackingErrorVisualizationImplementationConnectivityInformationParcellation
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
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