Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging

Jie Fu; Yingli Yang; Kamal Singhrao; Dan Ruan; Fang‐I Chu; Daniel A. Low; John H. Lewis

doi:10.1002/mp.13672

Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging

Medical Physics ◽

10.1002/mp.13672 ◽

2019 ◽

Vol 46 (9) ◽

pp. 3788-3798 ◽

Cited By ~ 14

Author(s):

Jie Fu ◽

Yingli Yang ◽

Kamal Singhrao ◽

Dan Ruan ◽

Fang‐I Chu ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Computed Tomography ◽

Neural Networks ◽

Deep Learning ◽

Magnetic Resonance ◽

Convolutional Neural Networks ◽

Learning Approaches ◽

Resonance Imaging ◽

2D And 3D

Deep learning in nasopharyngeal carcinoma: A retrospective cohort study of 3D convolutional neural networks on magnetic resonance imaging

Annals of Oncology ◽

10.1093/annonc/mdz252.057 ◽

2019 ◽

Vol 30 ◽

pp. v471

Author(s):

M.Y. Qiang ◽

X. Lv ◽

C.F. Li ◽

K.Y. Liu ◽

X. Chen ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Neural Networks ◽

Cohort Study ◽

Deep Learning ◽

Magnetic Resonance ◽

Nasopharyngeal Carcinoma ◽

Convolutional Neural Networks ◽

Retrospective Cohort Study ◽

Retrospective Cohort ◽

Resonance Imaging

Deep learning classification of inverted papilloma malignant transformation using 3D convolutional neural networks and magnetic resonance imaging

International Forum of Allergy & Rhinology ◽

10.1002/alr.22958 ◽

2022 ◽

Author(s):

George S. Liu ◽

Angela Yang ◽

Dayoung Kim ◽

Andrew Hojel ◽

Diana Voevodsky ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Neural Networks ◽

Deep Learning ◽

Magnetic Resonance ◽

Malignant Transformation ◽

Convolutional Neural Networks ◽

Inverted Papilloma ◽

Resonance Imaging

Editorial on “Convolutional Neural Networks for Automated Classification of Prostate Multiparametric Magnetic Resonance Imaging Based on Image Quality”

Journal of Magnetic Resonance Imaging ◽

10.1002/jmri.27913 ◽

2021 ◽

Author(s):

Valdair F. Muglia ◽

Antonio Carlos Westphalen

Keyword(s):

Magnetic Resonance Imaging ◽

Neural Networks ◽

Magnetic Resonance ◽

Image Quality ◽

Convolutional Neural Networks ◽

Automated Classification ◽

Resonance Imaging ◽

Multiparametric Magnetic Resonance Imaging

Early Identification of Alzheimer’s Disease Using an Ensemble of 3D Convolutional Neural Networks and Magnetic Resonance Imaging

Advances in Brain Inspired Cognitive Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-030-00563-4_29 ◽

2018 ◽

pp. 303-311

Author(s):

Yuanyuan Chen ◽

Haozhe Jia ◽

Zhaowei Huang ◽

Yong Xia

Keyword(s):

Magnetic Resonance Imaging ◽

Alzheimer’S Disease ◽

Neural Networks ◽

Alzheimer's Disease ◽

Magnetic Resonance ◽

Convolutional Neural Networks ◽

Early Identification ◽

Resonance Imaging

Efficiency Improvement in a Busy Radiology Practice: Determination of Musculoskeletal Magnetic Resonance Imaging Protocol Using Deep-Learning Convolutional Neural Networks

Journal of Digital Imaging ◽

10.1007/s10278-018-0066-y ◽

2018 ◽

Vol 31 (5) ◽

pp. 604-610 ◽

Cited By ~ 23

Author(s):

Young Han Lee

Keyword(s):

Magnetic Resonance Imaging ◽

Neural Networks ◽

Deep Learning ◽

Magnetic Resonance ◽

Efficiency Improvement ◽

Resonance Imaging ◽

Imaging Protocol ◽

Radiology Practice ◽

Magnetic Resonance Imaging Protocol

Automated Segmentation of the Right Ventricle from Magnetic Resonance Imaging Using Deep Convolutional Neural Networks

Lecture Notes in Computer Science - Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge ◽

10.1007/978-3-030-93722-5_37 ◽

2022 ◽

pp. 344-351

Author(s):

Kumaradevan Punithakumar ◽

Adam Carscadden ◽

Michelle Noga

Keyword(s):

Magnetic Resonance Imaging ◽

Neural Networks ◽

Magnetic Resonance ◽

Right Ventricle ◽

Convolutional Neural Networks ◽

Automated Segmentation ◽

Resonance Imaging ◽

Deep Convolutional Neural Networks ◽

The Right

Spinal Stenosis Grading in Magnetic Resonance Imaging Using Deep Convolutional Neural Networks

Spine ◽

10.1097/brs.0000000000003377 ◽

2020 ◽

Vol 45 (12) ◽

pp. 804-812 ◽

Cited By ~ 1

Author(s):

Dongkyu Won ◽

Hyun-Joo Lee ◽

Suk-Joong Lee ◽

Sang Hyun Park

Keyword(s):

Magnetic Resonance Imaging ◽

Neural Networks ◽

Magnetic Resonance ◽

Spinal Stenosis ◽

Convolutional Neural Networks ◽

Resonance Imaging ◽

Deep Convolutional Neural Networks

189. Spinal stenosis grading in magnetic resonance imaging using deep convolutional neural networks

The Spine Journal ◽

10.1016/j.spinee.2020.05.600 ◽

2020 ◽

Vol 20 (9) ◽

pp. S93-S94

Author(s):

Suk-Joong Lee ◽

Hyun-Joo Lee

Keyword(s):

Magnetic Resonance Imaging ◽

Neural Networks ◽

Magnetic Resonance ◽

Spinal Stenosis ◽

Convolutional Neural Networks ◽

Resonance Imaging ◽

Deep Convolutional Neural Networks

Dyslexia detection using 3D convolutional neural networks and functional magnetic resonance imaging

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2020.105726 ◽

2020 ◽

Vol 197 ◽

pp. 105726

Author(s):

Sofia Zahia ◽

Begonya Garcia-Zapirain ◽

Ibone Saralegui ◽

Begoña Fernandez-Ruanova

Keyword(s):

Magnetic Resonance Imaging ◽

Neural Networks ◽

Magnetic Resonance ◽

Functional Magnetic Resonance Imaging ◽

Convolutional Neural Networks ◽

Functional Magnetic Resonance ◽

Resonance Imaging

P6482Measurement of left atrial size by non-contrast computed tomography, 2D and 3D transthoracic echocardiography compared to cardiac magnetic resonance imaging

European Heart Journal ◽

10.1093/eurheartj/ehy566.p6482 ◽

2018 ◽

Vol 39 (suppl_1) ◽

Author(s):

M H Fredgart ◽

R Carter Storch ◽

J E Moeller ◽

K A Oevrehus ◽

R Pecini ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Computed Tomography ◽

Cardiac Magnetic Resonance ◽

Magnetic Resonance ◽

Left Atrial ◽

Left Atrial Size ◽

Resonance Imaging ◽

Atrial Size ◽

3D Transthoracic Echocardiography ◽

2D And 3D