2022
Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction
Aboian M, Bousabarah K, Kazarian E, Zeevi T, Holler W, Merkaj S, Petersen G, Bahar R, Subramanian H, Sunku P, Schrickel E, Bhawnani J, Zawalich M, Mahajan A, Malhotra A, Payabvash S, Tocino I, Lin M, Westerhoff M. Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction. Frontiers In Neuroscience 2022, 16: 860208. PMID: 36312024, PMCID: PMC9606757, DOI: 10.3389/fnins.2022.860208.Peer-Reviewed Original ResearchBrain tumor segmentationMedical imagesFeature extractionTumor segmentationRadiomic feature extractionDiagnostic workstationDeep learning-based algorithmPatient's medical imagesLearning-based algorithmFeature extraction toolImage processing algorithmsYale New Haven HealthGround truth dataImage annotationAI-segmentationAI algorithmsArtificial intelligenceEnd workflowProcessing algorithmsPicture archivingLarge datasetsLarge expertManual modificationInternal datasetManual segmentationDevelopment of a workflow efficient PACS based automated brain tumor segmentation and radiomic feature extraction for clinical implementation (N2.003)
Aboian M, Bousabarah K, Kazarian E, Zeevi T, Holler W, Merkaj S, Petersen G, Bahar R, Subramanian H, Sunku P, Schrickel E, Mahajan A, Malhotra A, Payabvash S, Tocino I, Lin M, Westerhoff M. Development of a workflow efficient PACS based automated brain tumor segmentation and radiomic feature extraction for clinical implementation (N2.003). Neurology 2022, 98 DOI: 10.1212/wnl.98.18_supplement.3146.Peer-Reviewed Original Research
2021
NIMG-71. IDENTIFYING CLINICALLY APPLICABLE MACHINE LEARNING ALGORITHMS FOR GLIOMA SEGMENTATION USING A SYSTEMATIC LITERATURE REVIEW
Tillmanns N, Lum A, Brim W, Subramanian H, Lin M, Bousabarah K, Malhotra A, cui J, Brackett A, Payabvash S, Ikuta I, Johnson M, Turowski B, Aboian M. NIMG-71. IDENTIFYING CLINICALLY APPLICABLE MACHINE LEARNING ALGORITHMS FOR GLIOMA SEGMENTATION USING A SYSTEMATIC LITERATURE REVIEW. Neuro-Oncology 2021, 23: vi145-vi145. PMCID: PMC8598815, DOI: 10.1093/neuonc/noab196.568.Peer-Reviewed Original ResearchConvolutional neural networkSegmentation of gliomasSupport vector machineGlioma segmentationDeep learningMachine learningLikelihood of overfittingMachine Learning AlgorithmsArtificial intelligenceLearning algorithmDice scoreML algorithmsTumor segmentationNeural networkVector machineCommon algorithmsSegmentationSame datasetML methodsTCIA datasetAlgorithmData acquisitionAccuracy reportingHigh accuracyLearning