2022
Identifying 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
A Fully Automated Method for Segmenting Arteries and Quantifying Vessel Radii on Magnetic Resonance Angiography Images of Varying Projection Thickness
Avadiappan S, Payabvash S, Morrison MA, Jakary A, Hess CP, Lupo JM. A Fully Automated Method for Segmenting Arteries and Quantifying Vessel Radii on Magnetic Resonance Angiography Images of Varying Projection Thickness. Frontiers In Neuroscience 2020, 14: 537. PMID: 32612496, PMCID: PMC7308498, DOI: 10.3389/fnins.2020.00537.Peer-Reviewed Original ResearchAutomatic segmentationManual segmentationDice similarity coefficientEntire 3D volumeSegmentation of vesselsMagnetic resonance angiography imagesSegmentation accuracyImage processingSegmentation algorithmSynthetic datasetsF-scoreRobust segmentationDifferent noise levelsNovel algorithmSegmentationFrangi filterPrior methodsJaccard indexNoisy conditionsLow contrastMRA datasetsDatasetAutomated methodSimilarity coefficientAlgorithmMachine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings
Payabvash S, Aboian M, Tihan T, Cha S. Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings. Frontiers In Oncology 2020, 10: 71. PMID: 32117728, PMCID: PMC7018938, DOI: 10.3389/fonc.2020.00071.Peer-Reviewed Original ResearchDecision tree modelDifferent machineTree modelAccurate classification rateAveraged AUCClassification algorithmsTraining datasetRandom forestDecision treeClassification rateRandom forest modelMachineAlgorithmTerminal nodesHigh accuracyForest modelDecision modelHistogram analysisDichotomized classificationClassificationIntra-axial posterior fossa tumorsAccuracyDatasetGreater accuracyNodes