2024
Mine yOur owN Anatomy: Revisiting Medical Image Segmentation With Extremely Limited Labels
You C, Dai W, Liu F, Min Y, Dvornek N, Li X, Clifton D, Staib L, Duncan J. Mine yOur owN Anatomy: Revisiting Medical Image Segmentation With Extremely Limited Labels. IEEE Transactions On Pattern Analysis And Machine Intelligence 2024, 46: 11136-11151. PMID: 39269798, DOI: 10.1109/tpami.2024.3461321.Peer-Reviewed Original ResearchMedical image segmentationImage segmentationMedical image segmentation frameworkContext of medical image segmentationLong-tailed class distributionStrong data augmentationsIntra-class variationsSemi-supervised settingData imbalance issueImage segmentation frameworkMedical image analysisMedical image dataSupervision signalsContrastive learningBenchmark datasetsUnsupervised mannerLabel setsData augmentationSegmentation frameworkDomain expertisePseudo-codeImbalance issueModel trainingMedical imagesSegmentation model
2023
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective.
You C, Dai W, Min Y, Liu F, Clifton D, Zhou S, Staib L, Duncan J. Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective. Advances In Neural Information Processing Systems 2023, 36: 9984-10021. PMID: 38813114, PMCID: PMC11136570.Peer-Reviewed Original ResearchMedical image segmentationContrastive learningImage segmentationSemi-supervised medical image segmentationSemi-supervised contrastive learningSelf-supervised objectiveSemantic segmentation datasetsSemi-supervised methodGround-truth labelsQuality of visual representationSafety-critical tasksSegmentation datasetTail classesSegmentation taskLabel setsTruth labelsCL frameworkNegative examplesModel collapseVariance-reductionVariance-reduction techniquesVisual representationTaskLearningPairs of samplesImplicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts
You C, Dai W, Min Y, Staib L, Duncan J. Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts. Lecture Notes In Computer Science 2023, 14222: 561-571. PMID: 38840671, PMCID: PMC11151725, DOI: 10.1007/978-3-031-43898-1_54.Peer-Reviewed Original ResearchMedical image segmentationImage segmentationSegmentation methodPixel-level featuresComputer graphics problemImplicit neural representationsGrid-based representationMedical segmentationRendering frameworkSegmentation predictionsEnd mannerCorrelated contentCompetitive performance improvementsGraphics problemsSegmentationPoint representationPerformance improvementRegular gridSuch informationRepresentationConvolution operatorsExpertsComplex signalsRenderingFeatures
2022
Class-Aware Adversarial Transformers for Medical Image Segmentation.
You C, Zhao R, Liu F, Dong S, Chinchali S, Topcu U, Staib L, Duncan J. Class-Aware Adversarial Transformers for Medical Image Segmentation. Advances In Neural Information Processing Systems 2022, 35: 29582-29596. PMID: 37533756, PMCID: PMC10395073.Peer-Reviewed Original ResearchMedical image segmentationImage segmentationMedical image analysis domainMedical image analysis tasksImage analysis domainMedical image datasetsImage analysis tasksModel’s inner workingsTransformer-based approachTransformer-based modelsAdversarial training strategyRich semantic contextSegmentation label mapsLong-range dependenciesMulti-scale representationAnalysis tasksImage datasetsTransfer learningFeature representationInner workingsSegmentation accuracyCorrelated contentTransformer moduleLabel mapsInformation lossIncremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation
You C, Xiang J, Su K, Zhang X, Dong S, Onofrey J, Staib L, Duncan J. Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation. Lecture Notes In Computer Science 2022, 13573: 3-16. PMID: 37415747, PMCID: PMC10323962, DOI: 10.1007/978-3-031-18523-6_1.Peer-Reviewed Original ResearchIncremental learningMedical image segmentation tasksMulti-site datasetImage segmentation tasksMedical image segmentationProstate MRI SegmentationComputation resourcesMedical datasetsSegmentation taskImage segmentationSegmentation frameworkEmbedding featuresBenchmark datasetsMRI segmentationTraining dataTarget domainLearning approachPractical deploymentDomain-specific expertiseCompetitive performanceDatasetTraining schemePrior workSegmentationSingle modelSimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation
You C, Zhou Y, Zhao R, Staib L, Duncan JS. SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation. IEEE Transactions On Medical Imaging 2022, 41: 2228-2237. PMID: 35320095, PMCID: PMC10325835, DOI: 10.1109/tmi.2022.3161829.Peer-Reviewed Original ResearchConceptsMedical image segmentationImage segmentationSemi-supervised medical image segmentationRobust Medical Image SegmentationMedical image analysisUnsupervised training strategyAtrial Segmentation ChallengeLearning-based approachMedical image synthesisAverage Dice scoreSemi-supervised approachPair-wise similarityContrastive objectiveData augmentationSegmentation challengePopular datasetsDice scoreSemantic informationDistillation frameworkSegmentation accuracyDownstream tasksImage synthesisPrevious best resultSupervised counterpartMedical data
2020
Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation
Onofrey JA, Staib LH, Huang X, Zhang F, Papademetris X, Metaxas D, Rueckert D, Duncan JS. Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation. Annual Review Of Biomedical Engineering 2020, 22: 1-27. PMID: 32169002, PMCID: PMC9351438, DOI: 10.1146/annurev-bioeng-060418-052147.Peer-Reviewed Original Research
1996
Deformable boundary finding in medical images by integrating gradient and region information
Chakraborty A, Staib L, Duncan J. Deformable boundary finding in medical images by integrating gradient and region information. IEEE Transactions On Medical Imaging 1996, 15: 859-870. PMID: 18215965, DOI: 10.1109/42.544503.Peer-Reviewed Original ResearchBoundary findingMedical imagesHomogeneous region-classified areaBiomedical image analysisGray level homogeneityRegion-based segmentationReal medical imagesComputational overheadImage segmentationRegion informationShape informationPoor initializationPerceptual notionsImage analysisNumber of experimentsSegmentationVariety of limitationsGreen's theoremImagesUnified approachAuthors' approachKey issuesNew approachOverheadInformation
1994
Deformable boundary finding influenced by region homogeneity
Chakraborty A, Staib L, Duncan J. Deformable boundary finding influenced by region homogeneity. 2015 IEEE Conference On Computer Vision And Pattern Recognition (CVPR) 1994, 624-627. DOI: 10.1109/cvpr.1994.323790.Peer-Reviewed Original ResearchHomogeneous region-classified areaGray level homogeneityBoundary findingBiomedical image analysisRegion-based segmentationImage segmentationShape informationGreen's theoremPoor initializationConventional methodsPerceptual notionsRegion homogeneityImage analysisVariety of limitationsSegmentationUnified approachKey issuesAn integrated approach to boundary finding in medical images
Chakraborty A, Staib L, Duncan J. An integrated approach to boundary finding in medical images. 1994, 13-22. DOI: 10.1109/bia.1994.315870.Peer-Reviewed Original ResearchBoundary findingMedical imagesHomogeneous region-classified areaBiomedical image analysisGray level homogeneityReal medical imagesImage segmentationShape informationPoor initializationPerceptual notionsImage analysisNumber of experimentsSegmentationVariety of limitationsConventional gradientImagesUnified approachAuthors' approachKey issuesNew approachGreen's theoremConventional methodsIntegrated approachInitializationFinder