Z. Akkus

发表

B. Erickson, P. Korfiatis, Z. Akkus, 2017, Radiographics : a review publication of the Radiological Society of North America, Inc.

Toolkits and Libraries for Deep Learning

Bradley J. Erickson, Panagiotis Korfiatis, Zeynettin Akkus, 2017, Journal of Digital Imaging.

Semiautomated Segmentation of Polycystic Kidneys in T2-Weighted MR Images.

Marie E. Edwards, B. Erickson, P. Korfiatis, 2016, AJR. American journal of roentgenology.

What Does Deep Learning See? Insights From a Classifier Trained to Predict Contrast Enhancement Phase From CT Images.

Alexander D. Weston, Kenneth A. Philbrick, B. Erickson, 2018, AJR. American journal of roentgenology.

Image Analysis for Contrast Enhanced Ultrasound Carotid Plaque Imaging

Z. Akkus, 2014 .

Assessment of carotid atherosclerosis, intraplaque neovascularization, and plaque ulceration using quantitative contrast-enhanced ultrasound in asymptomatic patients with diabetes mellitus.

Johan G Bosch, Zeynettin Akkus, Guillaume Renaud, 2014, European heart journal cardiovascular Imaging.

New quantification methods for carotid intraplaque neovascularization in contrast enhanced ultrasound

Nico de Jong, Johan G. Bosch, Zeynettin Akkus, 2013, 2013 IEEE International Ultrasonics Symposium (IUS).

Robust brain extraction tool for CT head images

Bradley J. Erickson, Zeynettin Akkus, Kenneth A. Philbrick, 2020, Neurocomputing.

Thyroid Nodule Size as a Predictor of Malignancy in Follicular and Hurthle Neoplasms

M. Castro, B. Erickson, Z. Akkus, 2021, Asian Pacific journal of cancer prevention : APJCP.

Assessment of subclinical atherosclerosis and intraplaque neovascularization using quantitative contrast-enhanced ultrasound in patients with familial hypercholesterolemia.

Johan G Bosch, Zeynettin Akkus, Stijn C H van den Oord, 2013, Atherosclerosis.

Fully automated carotid plaque segmentation in combined contrast-enhanced and B-mode ultrasound.

Nico de Jong, Diego D B Carvalho, Wiro J Niessen, 2015, Ultrasound in medicine & biology.

Far-wall pseudoenhancement during contrast-enhanced ultrasound of the carotid arteries: clinical description and in vitro reproduction.

Nico de Jong, Zeynettin Akkus, Vijay Shamdasani, 2012, Ultrasound in medicine & biology.

Dynamic assessment of carotid plaque motion

Zeynettin Akkus, Kumar V. Ramnarine, Z. Akkus, 2010 .

Interactive segmentation of medical images through fully convolutional neural networks pdf

Daguang Xu, Holger Roth, Bradley J. Erickson, 2019, ArXiv.

Semi-automated segmentation of pre-operative low grade gliomas in magnetic resonance imaging

Joshua D. Warner, B. Erickson, P. Korfiatis, 2015, Cancer Imaging.

Automated measurement of total kidney volume from 3D ultrasound images of patients affected by polycystic kidney disease and comparison to MR measurements

Marie E. Edwards, Adriana V. Gregory, B. Erickson, 2022, Abdominal Radiology.

Quantification of bound microbubbles in ultrasound molecular imaging

M. Daemen, Z. Akkus, J. Bosch, 2015, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

Quantification of targeted microbubbles in contrast enhanced ultrasound

M. Daemen, Z. Akkus, J. Bosch, 2013, 2013 IEEE International Ultrasonics Symposium (IUS).

Automated measurement of total kidney volume from 3D ultrasound images of patients affected by polycystic kidney disease and comparison to MR measurements

Marie E. Edwards, Adriana V. Gregory, B. Erickson, 2022, Abdominal Radiology.

Quantitative Contrast-Enhanced Ultrasound of Intraplaque Neovascularization in Patients with Carotid Atherosclerosis

D. Adam, Z. Akkus, E. Sijbrands, 2014, Ultraschall in der Medizin.

Deep Learning Estimation of Median Nerve Volume Using Ultrasound Imaging in a Human Cadaver Model.

Z. Akkus, J. Jagtap, Tomoyuki Kuroiwa, 2022, Ultrasound in medicine & biology.

Classification of Monocytes, Promonocytes and Monoblasts Using Deep Neural Network Models: An Area of Unmet Need in Diagnostic Hematopathology

Z. Akkus, M. Salama, M. Patnaik, 2021, Journal of clinical medicine.

Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

Daniel L. Rubin, Bradley J. Erickson, Zeynettin Akkus, 2017, Journal of Digital Imaging.

Machine learning and augmented human intelligence use in histomorphology for haematolymphoid disorders.

L. Pantanowitz, Z. Akkus, M. Salama, 2021, Pathology.

Predicting Deletion of Chromosomal Arms 1p/19q in Low-Grade Gliomas from MR Images Using Machine Intelligence

Jirí Sedlár, Bradley J. Erickson, Zeynettin Akkus, 2017, Journal of Digital Imaging.

RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning

Bradley J. Erickson, Panagiotis Korfiatis, Timothy L. Kline, 2019, Journal of Digital Imaging.

Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI

Bradley J. Erickson, Panagiotis Korfiatis, Zeynettin Akkus, 2018, Journal of Digital Imaging.

A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence-Powered Ultrasound for Improving Clinical Workflow.

Bradley J Erickson, Zeynettin Akkus, Alexander D Weston, 2019, Journal of the American College of Radiology : JACR.

A Web/Cloud based Digital Pathology Platform Framework for AI Development and Deployment

Bryan J. Dangott, A. Nassar, Z. Akkus, 2022, bioRxiv.

Artificial intelligence for detecting mitral regurgitation using electrocardiography.

Z. Akkus, J. Kwon, Kyung-Hee Kim, 2020, Journal of electrocardiology.

Carotid Intraplaque Neovascularization Quantification Software (CINQS)

Zeynettin Akkus, Gerard van Burken, Nico de Jong, 2015, IEEE Journal of Biomedical and Health Informatics.

Quantitative analysis of ultrasound contrast flow behavior in carotid plaque neovasculature.

Nico de Jong, Johan G Bosch, Zeynettin Akkus, 2012, Ultrasound in medicine & biology.

Deep Learning in Radiology: Does One Size Fit All?

Bradley J. Erickson, Panagiotis Korfiatis, Timothy L. Kline, 2018, Journal of the American College of Radiology : JACR.

Extraction of brain tissue from CT head images using fully convolutional neural networks

Bradley J. Erickson, Zeynettin Akkus, Kenneth A. Philbrick, 2018, Medical Imaging.

Artificial Intelligence (AI)-Empowered Echocardiography Interpretation: A State-of-the-Art Review

Adelaide M. Arruda-Olson, Itzhak Z. Attia, P. Pellikka, 2021, Journal of clinical medicine.

Motion compensation method using dynamic programming for quantification of neovascularization in carotid atherosclerotic plaques with contrast enhanced ultrasound (CEUS)

Nico de Jong, Johan G. Bosch, Zeynettin Akkus, 2012, Medical Imaging.

Assessment of subclinical atherosclerosis using contrast-enhanced ultrasound.

Nico de Jong, Johan G Bosch, Zeynettin Akkus, 2013, European heart journal cardiovascular Imaging.

Impact of gender on the density of intraplaque neovascularization: a quantitative contrast-enhanced ultrasound study.

Z. Akkus, J. V. D. van der Burg, E. Sijbrands, 2014, Atherosclerosis.

Fully Automated Mitral Inflow Doppler Analysis Using Deep Learning

Zeynettin Akkus, Mohamed Y. Elwazir, Didem Oguz, 2020, 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE).

Fully Automated Segmentation of Bladder Sac and Measurement of Detrusor Wall Thickness from Transabdominal Ultrasound Images

Bae Hyung Kim, Mostafa Fatemi, Zeynettin Akkus, 2020, Sensors.

Statistical segmentation of carotid plaque neovascularization

Nico de Jong, Johan G. Bosch, Zeynettin Akkus, 2013, Medical Imaging.

Lumen Segmentation and Motion Estimation in B-Mode and Contrast-Enhanced Ultrasound Images of the Carotid Artery in Patients With Atherosclerotic Plaque

Stefan Klein, Wiro J. Niessen, Zeynettin Akkus, 2015, IEEE Transactions on Medical Imaging.

AI for Automated Segmentation and Characterization of Median Nerve Volume

B. Erickson, Z. Akkus, J. Jagtap, 2023, Journal of Medical and Biological Engineering.

Predicting 1p19q Chromosomal Deletion of Low-Grade Gliomas from MR Images using Deep Learning pdf

Jirí Sedlár, Bradley J. Erickson, Timothy L. Kline, 2016, ArXiv.

Far-wall pseudoenhancement during contrast-enhanced ultrasound of the carotid arteries: clinical description and in vitro reproduction.

Z. Akkus, F. T. ten Cate, E. Sijbrands, 2012, Ultrasound in medicine & biology.

Fully Automated and Robust Tracking of Transient Waves in Structured Anatomies Using Dynamic Programming.

Mostafa Fatemi, Bradley J Erickson, Zeynettin Akkus, 2016, Ultrasound in medicine & biology.

Machine Learning for Medical Imaging.

B. Erickson, P. Korfiatis, Z. Akkus, 2017, Radiographics : a review publication of the Radiological Society of North America, Inc.

Estimating 3D lumen centerlines of carotid arteries in free-hand acquisition ultrasound

Stefan Klein, Wiro J. Niessen, Aad van der Lugt, 2012, International Journal of Computer Assisted Radiology and Surgery.

Artificial Intelligence Advances in Transplant Pathology

A. Nassar, Z. Akkus, C. Alper, 2023, Bioengineering.

Fully Automated Segmentation of Head CT Neuroanatomy Using Deep Learning.

Bradley J. Erickson, Zeynettin Akkus, Gian Marco Conte, 2020, Radiology. Artificial intelligence.