2022
Flow-Based Visual Quality Enhancer for Super-Resolution Magnetic Resonance Spectroscopic Imaging
Dong S, Hangel G, Chen E, Sun S, Bogner W, Widhalm G, You C, Onofrey J, de Graaf R, Duncan J. Flow-Based Visual Quality Enhancer for Super-Resolution Magnetic Resonance Spectroscopic Imaging. Lecture Notes In Computer Science 2022, 13609: 3-13. DOI: 10.1007/978-3-031-18576-2_1.Peer-Reviewed Original ResearchAdversarial networkVisual qualityDeep learning-based super-resolution methodsLearning-based super-resolution methodsFlow-based modelImage visual qualityGenerative adversarial networkHigh visual qualitySuper-resolution methodSuper-resolved imagesGenerative modelHigh-resolution imagesImage modalitiesFlow-based methodNetworkLow spatial resolutionUncertainty estimationImagesPromising resultsEnhancer networkAnatomical informationHigh fidelityEssential toolDatasetQuality adjustment
2018
Liver Tissue Classification Using an Auto-context-based Deep Neural Network with a Multi-phase Training Framework
Zhang F, Yang J, Nezami N, Laage-gaupp F, Chapiro J, De Lin M, Duncan J. Liver Tissue Classification Using an Auto-context-based Deep Neural Network with a Multi-phase Training Framework. Lecture Notes In Computer Science 2018, 11075: 59-66. PMID: 32432233, PMCID: PMC7236808, DOI: 10.1007/978-3-030-00500-9_7.Peer-Reviewed Original ResearchNeural networkNovel deep convolutional neural networkStandard neural network approachesTraining frameworkDeep convolutional neural networkU-Net-like architectureTissue classificationConvolutional neural networkDeep neural networksNeural network approachSegmentation masksBenchmark methodsNetwork approachPatch-based strategyLearning spacesLiver tissue classificationMagnetic resonance imagesPromising resultsNetworkImagesPredictive modelClassificationFrameworkResonance imagesArchitecture
2010
Integrated Segmentation and Nonrigid Registration for Application in Prostate Image-Guided Radiotherapy
Lu C, Chelikani S, Chen Z, Papademetris X, Staib LH, Duncan JS. Integrated Segmentation and Nonrigid Registration for Application in Prostate Image-Guided Radiotherapy. Lecture Notes In Computer Science 2010, 13: 53-60. PMID: 20879214, DOI: 10.1007/978-3-642-15705-9_7.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsHumansImaging, Three-DimensionalMaleProstatic NeoplasmsRadiographic Image EnhancementRadiographic Image Interpretation, Computer-AssistedRadiotherapy, Computer-AssistedReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueSystems IntegrationTomography, X-Ray ComputedConceptsManual segmentationAutomatic segmentationImportant treatment parametersNonrigid registrationImage-guided radiotherapy systemReal patient dataNon-rigid registrationIntegrated SegmentationRegistration partRadiotherapy linear acceleratorSegmentationTreatment imagesImage qualityCone-beam CTTreatment parametersImagesPromising resultsPatient dataKey anatomical structuresLinear acceleratorRegistrationPrevious workRadiotherapy system