2021
Quantitative Automated Segmentation of Lipiodol Deposits on Cone-Beam CT Imaging Acquired during Transarterial Chemoembolization for Liver Tumors: A Deep Learning Approach
Malpani R, Petty CW, Yang J, Bhatt N, Zeevi T, Chockalingam V, Raju R, Petukhova-Greenstein A, Santana JG, Schlachter TR, Madoff DC, Chapiro J, Duncan J, Lin M. Quantitative Automated Segmentation of Lipiodol Deposits on Cone-Beam CT Imaging Acquired during Transarterial Chemoembolization for Liver Tumors: A Deep Learning Approach. Journal Of Vascular And Interventional Radiology 2021, 33: 324-332.e2. PMID: 34923098, PMCID: PMC8972393, DOI: 10.1016/j.jvir.2021.12.017.Peer-Reviewed Original ResearchCarcinoma, HepatocellularChemoembolization, TherapeuticCone-Beam Computed TomographyDeep LearningEthiodized OilHumansLiver NeoplasmsAnatomy-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 ResearchMeSH KeywordsAlgorithmsCarcinoma, HepatocellularChemoembolization, TherapeuticCone-Beam Computed TomographyHumansImage Processing, Computer-AssistedLiver NeoplasmsSpiral Cone-Beam Computed TomographyConceptsMultimodal 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
2007
A frequency‐based approach to locate common structure for 2D‐3D intensity‐based registration of setup images in prostate radiotherapy
Munbodh R, Chen Z, Jaffray DA, Moseley DJ, Knisely JP, Duncan JS. A frequency‐based approach to locate common structure for 2D‐3D intensity‐based registration of setup images in prostate radiotherapy. Medical Physics 2007, 34: 3005-3017. PMID: 17822009, PMCID: PMC2796184, DOI: 10.1118/1.2745235.Peer-Reviewed Original ResearchMeSH KeywordsCone-Beam Computed TomographyHumansImaging, Three-DimensionalMalePhantoms, ImagingProstateRadiotherapy, ConformalReproducibility of Results