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 coefficientAlgorithm
2018
A user-guided tool for semi-automated cerebral microbleed detection and volume segmentation: Evaluating vascular injury and data labelling for machine learning
Morrison MA, Payabvash S, Chen Y, Avadiappan S, Shah M, Zou X, Hess CP, Lupo JM. A user-guided tool for semi-automated cerebral microbleed detection and volume segmentation: Evaluating vascular injury and data labelling for machine learning. NeuroImage Clinical 2018, 20: 498-505. PMID: 30140608, PMCID: PMC6104340, DOI: 10.1016/j.nicl.2018.08.002.Peer-Reviewed Original ResearchConceptsData labelingTraining dataHigh-level feature extractionVolume segmentationComputer-aided detection algorithmComputer-aided detection methodsGround truth labelingCerebral microbleed detectionFalse positivesMachine learningFeature extractionSegmentation resultsDetection algorithmSophisticated machineTime usersAlgorithm performanceCMB detectionComputer aidMicrobleed detectionSegmentationTest setDetection methodSuperior performanceExtensive research effortsMachine