Generative Self-training for Cross-Domain Unsupervised Tagged-to-Cine MRI Synthesis
Liu X, Xing F, Stone M, Zhuo J, Reese T, Prince J, El Fakhri G, Woo J. Generative Self-training for Cross-Domain Unsupervised Tagged-to-Cine MRI Synthesis. Lecture Notes In Computer Science 2021, 12903: 138-148. PMID: 34734217, PMCID: PMC8562649, DOI: 10.1007/978-3-030-87199-4_13.Peer-Reviewed Original ResearchUnsupervised domain adaptationTarget domainUDA methodsImage synthesisProblem of domain shiftUnsupervised domain adaptation frameworkSelf-trainingTraining deep learning modelsVariational Bayes learningUnlabeled target domainAlternating optimization schemePseudo-label selectionDeep learning modelsContinuous value predictionPseudo-labelsDomain adaptationDomain shiftCross-domainSynthesis qualityBayes learningDiscrete histogramsPrediction confidenceLearning modelsGeneration taskOptimization schemeDual-Cycle Constrained Bijective Vae-Gan For Tagged-To-Cine Magnetic Resonance Image Synthesis
Liu X, Xing F, Prince J, Carass A, Stone M, Fakhri G, Woo J. Dual-Cycle Constrained Bijective Vae-Gan For Tagged-To-Cine Magnetic Resonance Image Synthesis. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2021, 00: 1448-1452. PMID: 34707796, PMCID: PMC8547333, DOI: 10.1109/isbi48211.2021.9433852.Peer-Reviewed Original ResearchMR image synthesisAdversarial trainingImage synthesisVAE-GANMR imagingTagged MR imagesCine MR imagingMoving organsSuperior performanceMagnetic resonance imagingAcquisition timeCycle reconstructionHealthy subjectsResonance imagingAnatomical resolutionComparison methodMotion analysisTagged magnetic resonance imagingTissue segmentationImagesScanning session