2024
Heteroscedastic Uncertainty Estimation Framework for Unsupervised Registration
Zhang X, Pak D, Ahn S, Li X, You C, Staib L, Sinusas A, Wong A, Duncan J. Heteroscedastic Uncertainty Estimation Framework for Unsupervised Registration. Lecture Notes In Computer Science 2024, 15002: 651-661. DOI: 10.1007/978-3-031-72069-7_61.Peer-Reviewed Original ResearchUnsupervised registrationReal-world medical imagesCollaborative training strategyMedical image datasetsDeep learning methodsAccurate displacement estimationSignal-to-noise ratioImage datasetsRegistration architectureLearning methodsMedical imagesTraining strategyNoise distributionUncertainty estimationWeighting schemeRegistration performanceSpatial domainEstimation frameworkInput-dependentUncertainty estimation frameworkUniform noise levelsDisplacement estimationFrameworkNoise levelUnsupervised
2023
DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT
Chen X, Zhou B, Xie H, Guo X, Zhang J, Duncan J, Miller E, Sinusas A, Onofrey J, Liu C. DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT. Medical Image Analysis 2023, 88: 102840. PMID: 37216735, PMCID: PMC10524650, DOI: 10.1016/j.media.2023.102840.Peer-Reviewed Original ResearchConceptsCross-modality registrationConvolutional layersCo-attention mechanismMultiple convolutional layersCo-attention moduleDifferent convolutional layersMedical image registrationInput data streamDeep learning strategiesLow registration errorIntensity-based registration methodCardiac SPECTΜ-mapsDeep learningFeature fusionData streamsInput imageSource codeFeature mapsNeural networkImage registrationSpatial featuresRegistration performanceRegistration methodInput information