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John Onofrey, PhD

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Assistant Professor of Radiology & Biomedical Imaging and of Urology

Research & Publications
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Selected Publications

Reliable Prostate Cancer Risk Mapping From MRI Using Targeted and Systematic Core Needle Biopsy HistopathologyZeevi T, Leapman M, Sprenkle P, Venkataraman R, Staib L, Onofrey J. Reliable Prostate Cancer Risk Mapping From MRI Using Targeted and Systematic Core Needle Biopsy Histopathology. IEEE Transactions On Biomedical Engineering 2024, 71: 1084-1091. PMID: 37874731, PMCID: PMC10901528, DOI: 10.1109/tbme.2023.3326799.
Patient-Specific Heart Geometry Modeling for Solid Biomechanics Using Deep LearningPak D, Liu M, Kim T, Liang L, Caballero A, Onofrey J, Ahn S, Xu Y, McKay R, Sun W, Gleason R, Duncan J. Patient-Specific Heart Geometry Modeling for Solid Biomechanics Using Deep Learning. IEEE Transactions On Medical Imaging 2024, 43: 203-215. PMID: 37432807, PMCID: PMC10764002, DOI: 10.1109/tmi.2023.3294128.
LiverHccSeg: A publicly available multiphasic MRI dataset with liver and HCC tumor segmentations and inter-rater agreement analysisGross 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.
Fast Reconstruction for Deep Learning PET Head Motion CorrectionZeng T, Zhang J, Lieffrig E, Cai Z, Chen F, You C, Naganawa M, Lu Y, Onofrey J. Fast Reconstruction for Deep Learning PET Head Motion Correction. 2023, 14229: 710-719. PMID: 38174207, PMCID: PMC10758999, DOI: 10.1007/978-3-031-43999-5_67.
Cross-Attention for Improved Motion Correction in Brain PETCai Z, Zeng T, Lieffrig E, Zhang J, Chen F, Toyonaga T, You C, Xin J, Zheng N, Lu Y, Duncan J, Onofrey J. Cross-Attention for Improved Motion Correction in Brain PET. 2023, 14312: 34-45. PMID: 38174216, PMCID: PMC10758996, DOI: 10.1007/978-3-031-44858-4_4.
Machine learning to predict future PSA in patients with prostate cancer managed with active surveillance.Ayed A, Saillard C, Onofrey J, Moon I, Chang S, Feldman A, Nayan M. Machine learning to predict future PSA in patients with prostate cancer managed with active surveillance. Journal Of Clinical Oncology 2023, 41: e17098-e17098. DOI: 10.1200/jco.2023.41.16_suppl.e17098.
DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CTChen X, Zhou B, Xie H, Guo X, Zhang J, Duncan J, Miller E, Sinusas A, Onofrey J, Liu C. DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT. Medical Image Analysis 2023, 88: 102840. PMID: 37216735, PMCID: PMC10524650, DOI: 10.1016/j.media.2023.102840.
Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learningKucukkaya 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.
PD38-08 AVOIDING UNNECESSARY TARGETED PROSTATE BIOPSIES USING MACHINE LEARNINGEsmaili R, Khajir G, Leapman M, Sprenkle P, Martin D, Onofrey J. PD38-08 AVOIDING UNNECESSARY TARGETED PROSTATE BIOPSIES USING MACHINE LEARNING. Journal Of Urology 2023, 209: e996. DOI: 10.1097/ju.0000000000003336.08.
Multi-Task Deep Learning and Uncertainty Estimation for Pet Head Motion CorrectionLieffrig E, Zeng T, Zhang J, Fontaine K, Fang X, Revilla E, Lu Y, Onofrey J. Multi-Task Deep Learning and Uncertainty Estimation for Pet Head Motion Correction. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2023, 00: 1-5. PMID: 38111738, PMCID: PMC10725741, DOI: 10.1109/isbi53787.2023.10230791.
Integrating Prostate Specific Antigen Density Biomarker Into Deep Learning Prostate MRI Lesion Segmentation ModelsZhong J, Staib L, Venkataraman R, Onofrey J. Integrating Prostate Specific Antigen Density Biomarker Into Deep Learning Prostate MRI Lesion Segmentation Models. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2023, 00: 1-5. PMID: 38090633, PMCID: PMC10711801, DOI: 10.1109/isbi53787.2023.10230418.
Deep learning of image-derived measures of body composition in pediatric, adolescent, and young adult lymphoma: association with late treatment effectsTram N, Chou T, Janse S, Bobbey A, Audino A, Onofrey J, Stacy M. Deep learning of image-derived measures of body composition in pediatric, adolescent, and young adult lymphoma: association with late treatment effects. European Radiology 2023, 33: 6599-6607. PMID: 36988714, DOI: 10.1007/s00330-023-09587-z.
Deep learning-based attenuation map generation with simultaneously reconstructed PET activity and attenuation and low-dose applicationShi L, Zhang J, Toyonaga T, Shao D, Onofrey J, Lu Y. Deep learning-based attenuation map generation with simultaneously reconstructed PET activity and attenuation and low-dose application. Physics In Medicine And Biology 2023, 68: 035014. PMID: 36584395, DOI: 10.1088/1361-6560/acaf49.
Co-attention spatial transformer network for unsupervised motion tracking and cardiac strain analysis in 3D echocardiographyAhn S, Ta K, Thorn S, Onofrey J, Melvinsdottir I, Lee S, Langdon J, Sinusas A, Duncan J. Co-attention spatial transformer network for unsupervised motion tracking and cardiac strain analysis in 3D echocardiography. Medical Image Analysis 2022, 84: 102711. PMID: 36525845, PMCID: PMC9812938, DOI: 10.1016/j.media.2022.102711.
Inter-Pass Motion Correction for Whole-Body Dynamic PET and Parametric ImagingGuo X, Wu J, Chen M, Liu Q, Onofrey J, Pucar D, Pang Y, Pigg D, Casey M, Dvornek N, Liu C. Inter-Pass Motion Correction for Whole-Body Dynamic PET and Parametric Imaging. IEEE Transactions On Radiation And Plasma Medical Sciences 2022, 7: 344-353. PMID: 37842204, PMCID: PMC10569406, DOI: 10.1109/trpms.2022.3227576.
An objective evaluation method for head motion estimation in PET—Motion corrected centroid-of-distributionSun 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.
Flow-Based Visual Quality Enhancer for Super-Resolution Magnetic Resonance Spectroscopic ImagingDong S, Hangel G, Chen E, Sun S, Bogner W, Widhalm G, You C, Onofrey J, de Graaf R, Duncan J. Flow-Based Visual Quality Enhancer for Super-Resolution Magnetic Resonance Spectroscopic Imaging. 2022, 13609: 3-13. DOI: 10.1007/978-3-031-18576-2_1.
Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI SegmentationYou C, Xiang J, Su K, Zhang X, Dong S, Onofrey J, Staib L, Duncan J. Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation. 2022, 13573: 3-16. PMID: 37415747, PMCID: PMC10323962, DOI: 10.1007/978-3-031-18523-6_1.
Multi-scale Super-Resolution Magnetic Resonance Spectroscopic Imaging with Adjustable SharpnessDong S, Hangel G, Bogner W, Widhalm G, Rössler K, Trattnig S, You C, de Graaf R, Onofrey J, Duncan J. Multi-scale Super-Resolution Magnetic Resonance Spectroscopic Imaging with Adjustable Sharpness. 2022, 13436: 410-420. DOI: 10.1007/978-3-031-16446-0_39.
Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modality Registration of Cardiac SPECT and CTChen X, Zhou B, Xie H, Guo X, Zhang J, Sinusas A, Onofrey J, Liu C. Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modality Registration of Cardiac SPECT and CT. 2022, 13436: 46-55. DOI: 10.1007/978-3-031-16446-0_5.
Atlas-Based Semantic Segmentation of Prostate ZonesZhang J, Venkataraman R, Staib L, Onofrey J. Atlas-Based Semantic Segmentation of Prostate Zones. 2022, 13435: 570-579. PMID: 38084296, PMCID: PMC10711803, DOI: 10.1007/978-3-031-16443-9_55.
Supervised Deep Learning for Head Motion Correction in PETZeng T, Zhang J, Revilla E, Lieffrig E, Fang X, Lu Y, Onofrey J. Supervised Deep Learning for Head Motion Correction in PET. 2022, 13434: 194-203. PMID: 38107622, PMCID: PMC10725740, DOI: 10.1007/978-3-031-16440-8_19.
Correction to: TVnet: Automated Time-Resolved Tracking of the Tricuspid Valve Plane in MRI Long-Axis Cine Images with a Dual-Stage Deep Learning PipelineGonzales R, Lamy J, Seemann F, Heiberg E, Onofrey J, Peters D. Correction to: TVnet: Automated Time-Resolved Tracking of the Tricuspid Valve Plane in MRI Long-Axis Cine Images with a Dual-Stage Deep Learning Pipeline. 2021, 12906: c1-c1. DOI: 10.1007/978-3-030-87231-1_60.
TVnet: Automated Time-Resolved Tracking of the Tricuspid Valve Plane in MRI Long-Axis Cine Images with a Dual-Stage Deep Learning PipelineGonzales R, Lamy J, Seemann F, Heiberg E, Onofrey J, Peters D. TVnet: Automated Time-Resolved Tracking of the Tricuspid Valve Plane in MRI Long-Axis Cine Images with a Dual-Stage Deep Learning Pipeline. 2021, 12906: 567-576. DOI: 10.1007/978-3-030-87231-1_55.
Weakly Supervised Deep Learning for Aortic Valve Finite Element Mesh Generation from 3D CT ImagesPak 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. 2021, 12729: 637-648. DOI: 10.1007/978-3-030-78191-0_49.
PROOF-OF-CONCEPT USE OF MACHINE LEARNING TO PREDICT TUMOR RECURRENCE OF EARLY-STAGE HEPATOCELLULAR CARCINOMA BEFORE THERAPY USING BASELINE MAGNETIC RESONANCE IMAGINGBatra R, Kuecuekkaya A, Zeevi T, Raju R, Chai N, Haider S, Elbanan M, Petukhova A, Lin ,, Onofrey J, Nowak M, Cooper K, Thomas E, Gebauer B, Staib L, Chapiro J. PROOF-OF-CONCEPT USE OF MACHINE LEARNING TO PREDICT TUMOR RECURRENCE OF EARLY-STAGE HEPATOCELLULAR CARCINOMA BEFORE THERAPY USING BASELINE MAGNETIC RESONANCE IMAGING. Transplantation 2020, 104: s43-s44. DOI: 10.1097/01.tp.0000698472.65040.1e.
Magnetic resonance image connectivity analysis provides evidence of central nervous system mode of action for parasacral transcutaneous electro neural stimulation - A pilot studyNetto JMB, Scheinost D, Onofrey JA, Franco I. Magnetic resonance image connectivity analysis provides evidence of central nervous system mode of action for parasacral transcutaneous electro neural stimulation - A pilot study. Journal Of Pediatric Urology 2020, 16: 536-542. PMID: 32873504, DOI: 10.1016/j.jpurol.2020.08.002.
LBP14 Proof-of-concept use of machine learning to predict tumor recurrence of early-stage hepatocellular carcinoma before therapy using baseline magnetic resonance imagingKücükkaya A, Zeevi T, Raju R, Chai N, Haider S, Elbanan M, Petukhova A, Lin M, Onofrey J, Nowak M, Cooper K, Thomas E, Gebauer B, Madoff D, Staib L, Batra R, Chapiro J. LBP14 Proof-of-concept use of machine learning to predict tumor recurrence of early-stage hepatocellular carcinoma before therapy using baseline magnetic resonance imaging. Journal Of Hepatology 2020, 73: s130-s131. DOI: 10.1016/s0168-8278(20)30775-3.
MP23-11 PREDICTION OF ADVERSE OUTCOMES AT RADICAL PROSTATECTOMY IN GRADE GROUP 2 AND 3 PROSTATE BIOPSIES USING MACHINE LEARNINGPaulson* N, Zeevi T, Papademetris M, Onofrey J, Sprenkle P, Humphrey P, Staib L, Levi A. MP23-11 PREDICTION OF ADVERSE OUTCOMES AT RADICAL PROSTATECTOMY IN GRADE GROUP 2 AND 3 PROSTATE BIOPSIES USING MACHINE LEARNING. Journal Of Urology 2020, 203 DOI: 10.1097/ju.0000000000000856.011.
Deep learning-based attenuation map generation for myocardial perfusion SPECTShi L, Onofrey JA, Liu H, Liu YH, Liu C. Deep learning-based attenuation map generation for myocardial perfusion SPECT. European Journal Of Nuclear Medicine And Molecular Imaging 2020, 47: 2383-2395. PMID: 32219492, DOI: 10.1007/s00259-020-04746-6.
Sparse Data–Driven Learning for Effective and Efficient Biomedical Image SegmentationOnofrey JA, Staib LH, Huang X, Zhang F, Papademetris X, Metaxas D, Rueckert D, Duncan JS. Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation. Annual Review Of Biomedical Engineering 2020, 22: 1-27. PMID: 32169002, PMCID: PMC9351438, DOI: 10.1146/annurev-bioeng-060418-052147.
Supervised Machine Learning in Oncology: A Clinician's GuideMurali 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.
A Novel Loss Function Incorporating Imaging Acquisition Physics for PET Attenuation Map Generation Using Deep LearningShi L, Onofrey J, Revilla E, Toyonaga T, Menard D, Ankrah J, Carson R, Liu C, Lu Y. A Novel Loss Function Incorporating Imaging Acquisition Physics for PET Attenuation Map Generation Using Deep Learning. 2019, 11767: 723-731. DOI: 10.1007/978-3-030-32251-9_79.
An investigation of quantitative accuracy for deep learning based denoising in oncological PETLu W, Onofrey JA, Lu Y, Shi L, Ma T, Liu Y, Liu C. An investigation of quantitative accuracy for deep learning based denoising in oncological PET. Physics In Medicine And Biology 2019, 64: 165019. PMID: 31307019, DOI: 10.1088/1361-6560/ab3242.
Noninvasive In Vivo Quantification of Adeno-Associated Virus Serotype 9–Mediated Expression of the Sodium/Iodide Symporter Under Hindlimb Ischemia and Neuraminidase Desialylation in Skeletal Muscle Using Single-Photon Emission Computed Tomography/Computed TomographyBoutagy NE, Ravera S, Papademetris X, Onofrey JA, Zhuang ZW, Wu J, Feher A, Stacy MR, French BA, Annex BH, Carrasco N, Sinusas AJ. Noninvasive In Vivo Quantification of Adeno-Associated Virus Serotype 9–Mediated Expression of the Sodium/Iodide Symporter Under Hindlimb Ischemia and Neuraminidase Desialylation in Skeletal Muscle Using Single-Photon Emission Computed Tomography/Computed Tomography. Circulation Cardiovascular Imaging 2019, 12: e009063. PMID: 31296047, PMCID: PMC6629470, DOI: 10.1161/circimaging.119.009063.
MP56-08 DOES USE OF A NORMALIZING ALGORITHM CHANGE KEY ANALOG UROFLOW CHARACTERISTICS?Onofrey J, Netto J, Franco* I. MP56-08 DOES USE OF A NORMALIZING ALGORITHM CHANGE KEY ANALOG UROFLOW CHARACTERISTICS? Journal Of Urology 2019, 201 DOI: 10.1097/01.ju.0000556647.77448.74.
Generalizable Multi-Site Training and Testing Of Deep Neural Networks Using Image NormalizationOnofrey 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.
Data-driven voluntary body motion detection and non-rigid event-by-event correction for static and dynamic PETLu Y, Gallezot JD, Naganawa M, Ren S, Fontaine K, Wu J, Onofrey JA, Toyonaga T, Boutagy N, Mulnix T, Panin VY, Casey ME, Carson RE, Liu C. Data-driven voluntary body motion detection and non-rigid event-by-event correction for static and dynamic PET. Physics In Medicine And Biology 2019, 64: 065002. PMID: 30695768, DOI: 10.1088/1361-6560/ab02c2.
Deep-learned placental vessel segmentation for intraoperative video enhancement in fetoscopic surgerySadda P, Imamoglu M, Dombrowski M, Papademetris X, Bahtiyar MO, Onofrey J. Deep-learned placental vessel segmentation for intraoperative video enhancement in fetoscopic surgery. International Journal Of Computer Assisted Radiology And Surgery 2018, 14: 227-235. PMID: 30484115, PMCID: PMC6438174, DOI: 10.1007/s11548-018-1886-4.
Deep Learning Retinal Vessel Segmentation from a Single Annotated Example: An Application of Cyclic Generative Adversarial Neural NetworksSadda P, Onofrey J, Papademetris X. Deep Learning Retinal Vessel Segmentation from a Single Annotated Example: An Application of Cyclic Generative Adversarial Neural Networks. 2018, 11043: 82-91. DOI: 10.1007/978-3-030-01364-6_10.
Better Feature Matching for Placental Panorama ConstructionSadda P, Onofrey J, Bahtiyar M, Papademetris X. Better Feature Matching for Placental Panorama Construction. 2018, 11076: 128-137. DOI: 10.1007/978-3-030-00807-9_13.
Segmenting the Brain Surface From CT Images With Artifacts Using Locally Oriented Appearance and Dictionary LearningOnofrey JA, Staib LH, Papademetris X. Segmenting the Brain Surface From CT Images With Artifacts Using Locally Oriented Appearance and Dictionary Learning. IEEE Transactions On Medical Imaging 2018, 38: 596-607. PMID: 30176584, PMCID: PMC6476428, DOI: 10.1109/tmi.2018.2868045.
Real-time computerized video enhancement for minimally invasive fetoscopic surgerySadda P, Onofrey J, Imamoglu M, Papademetris X, Qarni B, Bahtiyar MO. Real-time computerized video enhancement for minimally invasive fetoscopic surgery. Laparoscopic Endoscopic And Robotic Surgery 2018, 1: 27-32. PMID: 31080936, PMCID: PMC6508886, DOI: 10.1016/j.lers.2018.06.001.
A Fetal FMRI Specific Motion Correction Algorithm Using 2ND Order Edge FeaturesScheinost* D, Onofrey J, Kwon S, Cross S, Sze G, Ment L, Papademetris X. A Fetal FMRI Specific Motion Correction Algorithm Using 2ND Order Edge Features. 2018, 1288-1292. DOI: 10.1109/isbi.2018.8363807.
Respiratory Motion Compensation for PET/CT with Motion Information Derived from Matched Attenuation-Corrected Gated PET DataLu Y, Fontaine K, Mulnix T, Onofrey JA, Ren S, Panin V, Jones J, Casey ME, Barnett R, Kench P, Fulton R, Carson RE, Liu C. Respiratory Motion Compensation for PET/CT with Motion Information Derived from Matched Attenuation-Corrected Gated PET Data. Journal Of Nuclear Medicine 2018, 59: 1480-1486. PMID: 29439015, PMCID: PMC6126443, DOI: 10.2967/jnumed.117.203000.
Non-Rigid Event-by-Event Continuous Respiratory Motion Compensated List-Mode Reconstruction for PETChan C, Onofrey J, Jian Y, Germino M, Papademetris X, Carson RE, Liu C. Non-Rigid Event-by-Event Continuous Respiratory Motion Compensated List-Mode Reconstruction for PET. IEEE Transactions On Medical Imaging 2017, 37: 504-515. PMID: 29028189, PMCID: PMC7304524, DOI: 10.1109/tmi.2017.2761756.
Learning Non-rigid Deformations for Robust, Constrained Point-based Registration in Image-Guided MR-TRUS Prostate InterventionOnofrey JA, Staib LH, Sarkar S, Venkataraman R, Nawaf CB, Sprenkle PC, Papademetris X. Learning Non-rigid Deformations for Robust, Constrained Point-based Registration in Image-Guided MR-TRUS Prostate Intervention. Medical Image Analysis 2017, 39: 29-43. PMID: 28431275, PMCID: PMC5514316, DOI: 10.1016/j.media.2017.04.001.
MRI-TRUS Image Synthesis with Application to Image-Guided Prostate InterventionOnofrey J, Oksuz I, Sarkar S, Venkataraman R, Staib L, Papademetris X. MRI-TRUS Image Synthesis with Application to Image-Guided Prostate Intervention. 2016, 9968: 157-166. DOI: 10.1007/978-3-319-46630-9_16.
Learning intervention-induced deformations for non-rigid MR-CT registration and electrode localization in epilepsy patientsOnofrey JA, Staib LH, Papademetris X. Learning intervention-induced deformations for non-rigid MR-CT registration and electrode localization in epilepsy patients. NeuroImage Clinical 2015, 10: 291-301. PMID: 26900569, PMCID: PMC4724039, DOI: 10.1016/j.nicl.2015.12.001.
Segmenting the Brain Surface from CT Images with Artifacts Using Dictionary Learning for Non-rigid MR-CT RegistrationOnofrey JA, Staib LH, Papademetris X. Segmenting the Brain Surface from CT Images with Artifacts Using Dictionary Learning for Non-rigid MR-CT Registration. 2015, 24: 662-674. PMID: 26221711, PMCID: PMC5266617, DOI: 10.1007/978-3-319-19992-4_52.
Learning Nonrigid Deformations for Constrained Point-Based Registration for Image-Guided Mr-Trus Prostate InterventionOnofrey JA, Staib LH, Sarkar S, Venkataraman R, Papademetris X. Learning Nonrigid Deformations for Constrained Point-Based Registration for Image-Guided Mr-Trus Prostate Intervention. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2015, 2015: 1592-1595. PMID: 26405508, PMCID: PMC4578171, DOI: 10.1109/isbi.2015.7164184.
Low-Dimensional Non-Rigid Image Registration Using Statistical Deformation Models From Semi-Supervised Training DataOnofrey J, Papademetris X, Staib L. Low-Dimensional Non-Rigid Image Registration Using Statistical Deformation Models From Semi-Supervised Training Data. IEEE Transactions On Medical Imaging 2015, 34: 1522-1532. PMID: 25720017, PMCID: PMC8802338, DOI: 10.1109/tmi.2015.2404572.
Semi-supervised Learning of Nonrigid Deformations for Image RegistrationOnofrey J, Staib L, Papademetris X. Semi-supervised Learning of Nonrigid Deformations for Image Registration. 2014, 13-23. DOI: 10.1007/978-3-319-14104-6_2.
Semi-supervised Learning of Nonrigid Deformations for Image RegistrationOnofrey J, Staib L, Papademetris X. Semi-supervised Learning of Nonrigid Deformations for Image Registration. 2014, 8331: 13-23. DOI: 10.1007/978-3-319-05530-5_2.
Learning Nonrigid Deformations for Constrained Multi-modal Image RegistrationOnofrey JA, Staib LH, Papademetris X. Learning Nonrigid Deformations for Constrained Multi-modal Image Registration. 2013, 16: 171-178. PMID: 24505758, PMCID: PMC4044829, DOI: 10.1007/978-3-642-40760-4_22.
A VTK-based, CUDA-optimized Non-Parametric Vessel Detection MethodAlpoge 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.

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