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 modalityAnatomy-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