2021
Anatomy-guided multimodal registration by learning segmentation without ground truth: Application to intraprocedural CBCT/MR liver segmentation and registration
Zhou B, Augenfeld Z, Chapiro J, Zhou SK, Liu C, Duncan JS. Anatomy-guided multimodal registration by learning segmentation without ground truth: Application to intraprocedural CBCT/MR liver segmentation and registration. Medical Image Analysis 2021, 71: 102041. PMID: 33823397, PMCID: PMC8184611, DOI: 10.1016/j.media.2021.102041.Peer-Reviewed Original ResearchConceptsMultimodal registrationLiver segmentationLarge-scale manual annotationGround truthMultimodal image registrationMultimodal registration methodSegmentation networkDomain adaptationManual annotationSource modalityImage registrationRegistration frameworkSegmentationImage-guided interventionsRegistration methodMedical imagingDiagnostic medical imagingCorrect transformationLimited FOVStructure informationIntraprocedural CBCTImage qualitySegmenterExperimental resultsPatient data
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 domainDomain-Agnostic Learning with Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation
Yang J, Dvornek NC, Zhang F, Zhuang J, Chapiro J, Lin M, Duncan JS. Domain-Agnostic Learning with Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation. ICCV Workshops 2019, 00: 323-331. PMID: 34676308, PMCID: PMC8528125, DOI: 10.1109/iccvw.2019.00043.Peer-Reviewed Original ResearchDomain adaptationDisentangled representationsLiver segmentationTarget domainSource domainDeep learning modelsGenerative adversarial networkHuman interpretabilityLearning frameworkAdversarial networkDownstream tasksArt methodsSegmentation consistencyLearning modelAgnostic learningMeaningful representationCycleGANNew tasksAblation analysisDA taskDifferent modalitiesTaskSegmentationEmbeddingLearning
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