2023
Spatial Normalization to Improve Deep Learning-based Head Motion Correction in PET
Zhang J, Lieffrig E, Zeng T, You C, Cai Z, Toyonaga T, Lu Y, Onofrey J. Spatial Normalization to Improve Deep Learning-based Head Motion Correction in PET. 2023, 00: 1-1. DOI: 10.1109/nssmicrtsd49126.2023.10338387.Peer-Reviewed Original ResearchPredicting 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
2021
Weakly Supervised Deep Learning for Aortic Valve Finite Element Mesh Generation from 3D CT Images
Pak D, Liu M, Ahn S, Caballero A, Onofrey J, Liang L, Sun W, Duncan J. Weakly Supervised Deep Learning for Aortic Valve Finite Element Mesh Generation from 3D CT Images. Lecture Notes In Computer Science 2021, 12729: 637-648. DOI: 10.1007/978-3-030-78191-0_49.Peer-Reviewed Original ResearchSupervised deep learningTranscather aortic valve replacementDeep learningSegmentation labelsMesh generationCorrespondence accuracyHeavy assumptionsFinite element mesh generationMesh topologyVolumetric meshLow contrastSignificant bottleneckValve modelingProblem formulationPrediction modelModel performanceCT imagesDeformation strategyLarge amountImagesBottleneckLearningMeshFrameworkLabels