2024
Automated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics
Gross M, Huber S, Arora S, Ze’evi T, Haider S, Kucukkaya A, Iseke S, Kuhn T, Gebauer B, Michallek F, Dewey M, Vilgrain V, Sartoris R, Ronot M, Jaffe A, Strazzabosco M, Chapiro J, Onofrey J. Automated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics. European Radiology 2024, 34: 5056-5065. PMID: 38217704, PMCID: PMC11245591, DOI: 10.1007/s00330-023-10495-5.Peer-Reviewed Original ResearchMagnetic resonance imagingRadiomics feature extractionLiver volumetryIntraclass correlation coefficientRadiomic featuresLiver segmentationAutomated liver volumetryHepatocellular carcinoma patientsMann-Whitney U testAutomated liver segmentationManual segmentationQuantitative imaging biomarkersCarcinoma patientsRetrospective studyInstitutional databaseAnatomical localizationClinical relevanceManual volumetryMann-WhitneyU testExternal validationInternal test setImaging biomarkersInclusion criteriaResultsIn total
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
2020
Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning
Bousabarah K, Letzen B, Tefera J, Savic L, Schobert I, Schlachter T, Staib LH, Kocher M, Chapiro J, Lin M. Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning. Abdominal Radiology 2020, 46: 216-225. PMID: 32500237, PMCID: PMC7714704, DOI: 10.1007/s00261-020-02604-5.Peer-Reviewed Original ResearchConceptsDeep convolutional neural networkAverage false positive rateDice similarity coefficientU-NetDeep learning algorithmsConvolutional neural networkTest setMean Dice similarity coefficientRandom forest classifierDCNN methodDCNN approachDeep learningNet architectureLearning algorithmNeural networkLiver segmentationManual 3D segmentationForest classifierGround truthManual segmentationFalse positive rateCorresponding segmentationSegmentationMultiphasic contrast-enhanced MRIThresholding
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