Posterior estimation using deep learning: a simulation study of compartmental modeling in dynamic positron emission tomography
Liu X, Marin T, Amal T, Woo J, Fakhri G, Ouyang J. Posterior estimation using deep learning: a simulation study of compartmental modeling in dynamic positron emission tomography. Medical Physics 2022, 50: 1539-1548. PMID: 36331429, PMCID: PMC10087283, DOI: 10.1002/mp.16078.Peer-Reviewed Original ResearchConceptsConditional variational auto-encoderDeep learning approachNeural networkDeep learningMarkov chain Monte CarloVariational Bayesian inference frameworkLearning approachDeep learning-based approachVariational auto-encoderDeep neural networksLearning-based approachDynamic brain PET imagingPosterior distributionEstimate posterior distributionsBayesian inference frameworkAuto-encoderMedical imagesInference frameworkNetworkSimulation studyBrain PET imagingLearningPosterior estimatesInferior performanceImagesVariational inference for quantifying inter-observer variability in segmentation of anatomical structures
Liu X, Xing F, Marin T, Fakhri G, Woo J. Variational inference for quantifying inter-observer variability in segmentation of anatomical structures. Proceedings Of SPIE--the International Society For Optical Engineering 2022, 12032: 120321m-120321m-6. PMID: 36303579, PMCID: PMC9603619, DOI: 10.1117/12.2604547.Peer-Reviewed Original ResearchSegmentation mapImage dataVariational inference frameworkVariational autoencoder networkMedical image dataInherent information lossMagnetic Resonance (MRSegmentation datasetAutoencoder networkVariational inferenceLatent vectorsInformation lossManual annotationSegmentation methodAleatoric uncertaintyInference frameworkSacrificing accuracyExperimental resultsAnnotationOrgan boundariesInter-observer variabilityImagesELBOMapsSegments