2023
Direct respiratory motion correction of whole-body PET images using a deep learning framework incorporating spatial information
Miao T, Tsai Y, Zhou B, Menard D, Schleyer P, Hong I, Casey M, Liu C. Direct respiratory motion correction of whole-body PET images using a deep learning framework incorporating spatial information. Progress In Biomedical Optics And Imaging 2023, 12463: 124633x-124633x-9. DOI: 10.1117/12.2654472.Peer-Reviewed Original ResearchDeep learning frameworkRespiratory motion correctionMotion-corrected imagesLearning frameworkImage domainSpatial informationData-driven gating methodMotion correctionMotion detection techniqueGround truth imagesU-NetTruth imagesPET imagesData driving methodImage reconstructionWhole-body PET imagesMotion sensorsDetection techniquesExternal motion sensorsCross validationImagesConvenient mannerFrameworkRespiratory motionInformation
2021
Trends in Development of Novel Machine Learning Methods for the Identification of Gliomas in Datasets That Include Non-Glioma Images: A Systematic Review
Subramanian H, Dey R, Brim WR, Tillmanns N, Petersen G, Brackett A, Mahajan A, Johnson M, Malhotra A, Aboian M. Trends in Development of Novel Machine Learning Methods for the Identification of Gliomas in Datasets That Include Non-Glioma Images: A Systematic Review. Frontiers In Oncology 2021, 11: 788819. PMID: 35004312, PMCID: PMC8733688, DOI: 10.3389/fonc.2021.788819.Peer-Reviewed Original ResearchMachine learningIdentification of gliomasNovel machine learning methodMachine learning methodsAccuracy of algorithmsFive-fold cross validationDeep learningArtificial intelligenceGlioma imagesAlgorithm trainingNeural networkHeterogeneous datasetsLearning methodsAlgorithm testingTRIPOD criteriaNormal imagesAlgorithm developmentSame datasetAbnormal imagesDatasetLimited datasetAlgorithmSingle-institution datasetCross validationLearning
2014
Semi-supervised Learning of Nonrigid Deformations for Image Registration
Onofrey J, Staib L, Papademetris X. Semi-supervised Learning of Nonrigid Deformations for Image Registration. Lecture Notes In Computer Science 2014, 8331: 13-23. DOI: 10.1007/978-3-319-05530-5_2.Peer-Reviewed Original ResearchLarge medical image databasesSemi-supervised learning frameworkMedical image databasesStatistical deformation modelSemi-supervised learningIntensity-based registrationImage databaseLeave-one-out cross validationLearning frameworkSupervised registrationsMR datasetsImage registrationRegistration algorithmUnsupervised registrationsBrain imagesNonrigid transformationNonrigid deformationResearch communityBrain Atlas databaseVast quantitiesCross validationMR brainRegistrationLarge amountDatabase
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