2021
Anatomy-Constrained Contrastive Learning for Synthetic Segmentation Without Ground-Truth
Zhou B, Liu C, Duncan J. Anatomy-Constrained Contrastive Learning for Synthetic Segmentation Without Ground-Truth. Lecture Notes In Computer Science 2021, 12901: 47-56. DOI: 10.1007/978-3-030-87193-2_5.Peer-Reviewed Original ResearchSegmentation networkContrastive learningManual segmentationSuperior segmentation performanceObject of interestSynthetic SegmentationManual effortSegmentation performanceTraining dataUnsupervised adaptationImaging dataSource modalitySegmentationNetworkPrevious methodsLearningLarge amountSuccessful applicationPET imaging dataImagesObjectsCodeDataNew imaging modality
2019
Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation
Yang J, Dvornek NC, Zhang F, Chapiro J, Lin M, Duncan JS. Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation. Lecture Notes In Computer Science 2019, 11765: 255-263. PMID: 32377643, PMCID: PMC7202929, DOI: 10.1007/978-3-030-32245-8_29.Peer-Reviewed Original ResearchDice similarity coefficientDomain adaptationContent spaceDomain shiftTarget domainCross-modality domain adaptationUnsupervised domain adaptation methodsDiverse image generationLiver segmentation taskDeep learning modelsDifferent target domainUnlabeled target dataFeature-level informationUnsupervised domain adaptationDomain adaptation methodsMulti-phasic MRISegmentation taskSegmentation performanceSegmentation modelImage generationLiver segmentationStyle transferDisentangled representationsBetter generalizationSource domain
2012
Precise Segmentation of Multiple Organs in CT Volumes Using Learning-Based Approach and Information Theory
Lu C, Zheng Y, Birkbeck N, Zhang J, Kohlberger T, Tietjen C, Boettger T, Duncan JS, Zhou SK. Precise Segmentation of Multiple Organs in CT Volumes Using Learning-Based Approach and Information Theory. Lecture Notes In Computer Science 2012, 15: 462-469. PMID: 23286081, DOI: 10.1007/978-3-642-33418-4_57.Peer-Reviewed Original ResearchConceptsMarginal Space LearningCT volumesChallenging segmentation problemInformation-theoretic schemesLearning-based approachComputer-aided diagnosisExcellent segmentation accuracyRobust boundary detectionInformation theoryPelvic organ segmentationSteerable featuresChallenging datasetArt solutionsOrgan segmentationSegmentation problemSpace learningSegmentation performanceSegmentation accuracyPrecise segmentationBoundary detectionJensen-Shannon divergenceTheoretic schemeInference processDiverse sourcesPrevious state