2023
Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation
Avesta A, Hossain S, Lin M, Aboian M, Krumholz H, Aneja S. Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation. Bioengineering 2023, 10: 181. PMID: 36829675, PMCID: PMC9952534, DOI: 10.3390/bioengineering10020181.Peer-Reviewed Original ResearchLimited training dataDice scoreComputational memoryTraining dataBrain imagesDeep-learning methodsHigher Dice scoresSegmentation accuracyAuto-segmentation modelComputational speedPerformance metricsOne-sliceAuto-SegmentationBetter performanceConsecutive slicesImagesDeploymentLowest Dice scoresMemoryPerformanceTrainingMetricsModelAccuracyData
2021
OTHR-12. The development of machine learning algorithms for the differentiation of glioma and brain metastases – a systematic review
Brim W, Jekel L, Petersen G, Subramanian H, Zeevi T, Payabvash S, Bousabarah K, Lin M, Cui J, Brackett A, Mahajan A, Johnson M, Mahajan A, Aboian M. OTHR-12. The development of machine learning algorithms for the differentiation of glioma and brain metastases – a systematic review. Neuro-Oncology Advances 2021, 3: iii17-iii17. PMCID: PMC8351249, DOI: 10.1093/noajnl/vdab071.067.Peer-Reviewed Original ResearchConvolutional neural networkDeep learningML algorithmsMachine Learning AlgorithmsApplication of machineClassical ML algorithmsDevelopment of machineSupport vector machine algorithmVector machine algorithmArtificial intelligenceMachine learningSearch strategyDL modelsLearning algorithmFeature extractionNeural networkMachine algorithmAverage accuracyML methodsCML algorithmAlgorithmHigh accuracyLearningMachineAccuracy