2022
Development of a workflow efficient PACS based automated brain tumor segmentation and radiomic feature extraction for clinical implementation (N2.003)
Aboian M, Bousabarah K, Kazarian E, Zeevi T, Holler W, Merkaj S, Petersen G, Bahar R, Subramanian H, Sunku P, Schrickel E, Mahajan A, Malhotra A, Payabvash S, Tocino I, Lin M, Westerhoff M. Development of a workflow efficient PACS based automated brain tumor segmentation and radiomic feature extraction for clinical implementation (N2.003). Neurology 2022, 98 DOI: 10.1212/wnl.98.18_supplement.3146.Peer-Reviewed Original ResearchIdentifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries
Tillmanns N, Lum AE, Cassinelli G, Merkaj S, Verma T, Zeevi T, Staib L, Subramanian H, Bahar RC, Brim W, Lost J, Jekel L, Brackett A, Payabvash S, Ikuta I, Lin M, Bousabarah K, Johnson MH, Cui J, Malhotra A, Omuro A, Turowski B, Aboian MS. Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries. Neuro-Oncology Advances 2022, 4: vdac093. PMID: 36071926, PMCID: PMC9446682, DOI: 10.1093/noajnl/vdac093.Peer-Reviewed Original ResearchGlioma segmentationResearch algorithmSegmentation of gliomasHigh accuracy resultsML algorithmsApplicable machineAccuracy resultsTCIA datasetSegmentationAlgorithmMachinePatient dataSystematic literature reviewOverfittingData extractionDatasetBratDatabaseRecent advancesResearch literatureLimitationsExtractionCurrent research literatureMethod
2020
Automatic Multimodal Registration via Intraprocedural Cone-Beam CT Segmentation using MRI Distance Maps
Augenfeld Z, Lin M, Chapiro J, Duncan J. Automatic Multimodal Registration via Intraprocedural Cone-Beam CT Segmentation using MRI Distance Maps. 2020, 00: 1-4. DOI: 10.1109/isbi45749.2020.9098619.Peer-Reviewed Original ResearchRobust Point MatchingMultimodal registrationConvolutional neural networkSpatial informationDistance mapTarget image segmentationAutomatic multimodal registrationDense spatial informationMore medical imagesMedical imagesImaging domainImage segmentationSupervised fashionNeural networkTarget imageCT segmentationSegmented regionsSource imagesPoint matchingRegistration frameworkSegmentationSecond networkNetworkImage qualityImage-guided procedures
2019
Domain-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