2023
Image Intensity Normalization Benefits Deep Learning Brain PET Motion Correction
Lieffrig E, Zhang J, Zeng T, Cai Z, You C, Lu Y, Onofrey J. Image Intensity Normalization Benefits Deep Learning Brain PET Motion Correction. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10338194.Peer-Reviewed Original ResearchInput data normalizationImage intensity normalizationNeural network inputsMedical imaging researchPET motion correctionPre-processing stepMotion prediction errorMotion correctionIntensity normalizationNetwork inputsMotion predictionHead motion correctionInput dataTesting subjectsData normalizationEarly framesSuch methodsPrediction errorImaging researchDifferent normalization strategiesNormalization strategyMachineAlgorithmTaskValue analysisLiverHccSeg: A publicly available multiphasic MRI dataset with liver and HCC tumor segmentations and inter-rater agreement analysis
Gross M, Arora S, Huber S, Kücükkaya A, Onofrey J. LiverHccSeg: A publicly available multiphasic MRI dataset with liver and HCC tumor segmentations and inter-rater agreement analysis. Data In Brief 2023, 51: 109662. PMID: 37869619, PMCID: PMC10587725, DOI: 10.1016/j.dib.2023.109662.Peer-Reviewed Original ResearchTumor segmentation algorithmTumor segmentationSegmentation algorithmLiver segmentationManual segmentationTumor segmentation taskHigh-quality segmentationSegmentation taskSegmentation metricsSegmentation performanceAccurate segmentationRelevant metadataSegmentation agreementSegmentationMedical imagingFeature analysisExternal dataDatasetIntra-rater variabilityAlgorithmInnovative solutionsPredicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning
Kucukkaya A, Zeevi T, Chai N, Raju R, Haider S, Elbanan M, Petukhova-Greenstein A, Lin M, Onofrey J, Nowak M, Cooper K, Thomas E, Santana J, Gebauer B, Mulligan D, Staib L, Batra R, Chapiro J. Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning. Scientific Reports 2023, 13: 7579. PMID: 37165035, PMCID: PMC10172370, DOI: 10.1038/s41598-023-34439-7.Peer-Reviewed Original Research
2022
An objective evaluation method for head motion estimation in PET—Motion corrected centroid-of-distribution
Sun C, Revilla EM, Zhang J, Fontaine K, Toyonaga T, Gallezot JD, Mulnix T, Onofrey JA, Carson RE, Lu Y. An objective evaluation method for head motion estimation in PET—Motion corrected centroid-of-distribution. NeuroImage 2022, 264: 119678. PMID: 36261057, DOI: 10.1016/j.neuroimage.2022.119678.Peer-Reviewed Original ResearchConceptsMotion informationHardware-based methodsHead motion estimationPET image reconstructionMotion estimation methodData-driven methodPET raw dataHead motionMask segmentationFinal image qualityMotion estimationTracking hardwareDifferent motion estimation methodsBrain PET studiesGround truthImage reconstructionRaw dataNew algorithmObjective quality controlInaccurate motion informationImage qualityMotion correction algorithmAlgorithmMotion errorsCorrection algorithm
2020
Supervised Machine Learning in Oncology: A Clinician's Guide
Murali N, Kucukkaya A, Petukhova A, Onofrey J, Chapiro J. Supervised Machine Learning in Oncology: A Clinician's Guide. Digestive Disease Interventions 2020, 04: 073-081. PMID: 32869010, PMCID: PMC7456427, DOI: 10.1055/s-0040-1705097.Peer-Reviewed Original ResearchMachine learningSupervised machineSupervised machine learning methodsNew data processing technologiesLarge volume dataData processing technologySupervised machine learningMachine learning methodsSelf-improving modelElectronic health recordsLearning methodsHealth recordsComputer algorithmWidespread adoptionLearningMeaningful insightsMachineAlgorithmTechnologyTechniqueFrameworkInformationAdoptionDataApplications
2019
Generalizable Multi-Site Training and Testing Of Deep Neural Networks Using Image Normalization
Onofrey JA, Casetti-Dinescu DI, Lauritzen AD, Sarkar S, Venkataraman R, Fan RE, Sonn GA, Sprenkle PC, Staib LH, Papademetris X. Generalizable Multi-Site Training and Testing Of Deep Neural Networks Using Image Normalization. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2019, 00: 348-351. PMID: 32874427, PMCID: PMC7457546, DOI: 10.1109/isbi.2019.8759295.Peer-Reviewed Original ResearchDeep neural networksNeural networkDeep learning algorithmsProstate gland segmentationImage normalization methodGland segmentationLearning algorithmImage normalizationMulti-site dataIntensity normalization methodNormalization methodSingle-site dataAlgorithmNetworkPotential solutionsEquipment sourcesClinical adoptionSegmentationTrainingIntensity characteristicsRobustnessDataSite trainingMethodAdoption
2010
A VTK-based, CUDA-optimized Non-Parametric Vessel Detection Method
Alpoge L, Joshi A, Scheinost D, Onofrey J, Qian X, Papademetris X. A VTK-based, CUDA-optimized Non-Parametric Vessel Detection Method. The VTK Journal 2010 DOI: 10.54294/z1w0uu.Chapters