2025
Using Foundation Models as Pseudo-label Generators for Pre-clinical 4D Cardiac CT Segmentation
Rickmann A, Thorn S, Ahn S, Lee S, Uman S, Lysyy T, Burns R, Guerrera N, Spinale F, Burdick J, Sinusas A, Duncan J. Using Foundation Models as Pseudo-label Generators for Pre-clinical 4D Cardiac CT Segmentation. Lecture Notes In Computer Science 2025, 15673: 253-265. DOI: 10.1007/978-3-031-94562-5_23.Peer-Reviewed Original ResearchImage segmentationRobust medical image segmentationAccurate pseudo labelsPseudo-label generationSelf-training strategySelf-training approachMedical image segmentationEnhance segmentation accuracyCardiac image segmentationImprove segmentation qualitySelf-training processPseudo-labelsDomain shiftConsecutive framesCardiac image analysisDeep learningSegmentation qualitySegmentation accuracyModeling tasksIterative updateTemporal inconsistencyMotion trackingCT segmentationHuman datasetsImage analysisIncreasing angular sampling for dedicated cardiac SPECT scanner: Implementation with Deep Learning and Validation with human data
Xie H, Alashi A, Thorn S, Chen X, Zhou B, Sinusas A, Liu C. Increasing angular sampling for dedicated cardiac SPECT scanner: Implementation with Deep Learning and Validation with human data. Journal Of Nuclear Cardiology 2025, 102168. PMID: 39986346, DOI: 10.1016/j.nuclcard.2025.102168.Peer-Reviewed Original Research
2022
Increasing angular sampling through deep learning for stationary cardiac SPECT image reconstruction
Xie H, Thorn S, Chen X, Zhou B, Liu H, Liu Z, Lee S, Wang G, Liu YH, Sinusas AJ, Liu C. Increasing angular sampling through deep learning for stationary cardiac SPECT image reconstruction. Journal Of Nuclear Cardiology 2022, 30: 86-100. PMID: 35508796, DOI: 10.1007/s12350-022-02972-z.Peer-Reviewed Original ResearchConceptsDeep learningReconstruction qualityImage reconstructionDeep learning methodsDeep neural networksDeep learning resultsImage qualityNetwork trainingSPECT image reconstructionNeural networkLearning methodsHigh image resolutionImage volumesClinical softwareImage metricsImage resolutionReconstruction resultsImproved image qualityTesting dataLearning resultsNetwork resultsPhysical phantomStationary imagingDifferent subjectsLearning
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