PhD Candidate
E-mail: m [dot] zreik [at] umcutrecht [dot] nl
Phone: +31 88 75 50565
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In 2008 Majd obtained his Bachelor of Science degree in Biomedical Engineering at the Technion – Israel Institute of Technology, Haifa, Israel. In 2010 he received his Master’s Degree also in Biomedical Engineering at Tel Aviv University. His master’s thesis focused on signal processing techniques on in-vivo brain signals. From 2010 until 2015 he worked as algorithms engineer/team leader in the biomedical industry. In 2015 he started as a PhD-candidate at the Image Sciences Institute at UMC Utrecht where his main area of research is assessment of cardiovascular risk from Coronary CT Angiography (CCTA). Majd is interested in image processing, quantitative imaging and machine learning.


Journal Articles

1.

M. Zreik, R.W. van Hamersvelt, N. Khalili, J.M. Wolterink, M. Voskuil, M.A. Viergever, T. Leiner, I. Išgum

Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography Journal Article

Transactions on Medical Imaging, 39 (5), pp. 1545-1557, 2020.

Abstract | Links | BibTeX

2.

M. Zreik, R.W. van Hamersvelt, J.M. Wolterink, T. Leiner, M.A. Viergever, I. Išgum

A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography Journal Article

IEEE Transactions on Medical Imaging, 38 (7), 2018.

Abstract | Links | BibTeX

3.

R.W. van Hamersvelt*, M. Zreik*, M. Voskuil, M.A. Viergever, I. Išgum, T. Leiner

Deep learning analysis of left ventricular myocardium in CT angiographic intermediate degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis Journal Article

European Radiology, 29 (5), pp. 2350–2359, 2018, (*equal contribution).

Abstract | Links | BibTeX

4.

M. Zreik, N. Lessmann, R.W. van Hamersvelt, J.M. Wolterink, M. Voskuil, M.A. Viergever, T. Leiner, I. Išgum

Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis Journal Article

Medical Image Analysis, 44 , pp. 72-85, 2018.

Abstract | Links | BibTeX

5.

N. Lessmann, B. van Ginneken, M. Zreik, P.A. de Jong, B.D. de Vos, M.A. Viergever, I. Išgum

Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions Journal Article

IEEE Transactions on Medical Imaging, 37 (2), pp. 615-625, 2018.

Abstract | Links | BibTeX

Inproceedings

1.

M. Zreik, N. Hampe, T. Leiner, N. Khalili, J.M. Wolterink, M. Voskuil, M.A. Viergever, I. Išgum

Combined analysis of coronary arteries and the left ventricular myocardium in cardiac CT angiography for detection of patients with functionally significant stenosis Inproceedings

In: SPIE Medical Imaging, pp. 115961F, 2021.

Abstract | Links | BibTeX

2.

N. Khalili, E. Turk, M. Zreik, M.A. Viergever, M.J.N.L. Benders, I. Išgum

Generative adversarial network for segmentation of motion affected neonatal brain MRI Inproceedings

In: Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Lecture Notes in Computer Science, 2019.

Links | BibTeX

3.

S.G.M. van Velzen, M. Zreik, N. Lessmann, M.A. Viergever, P.A. de Jong, H.M. Verkooijen, I. Išgum

Direct prediction of cardiovascular mortality from low-dose chest CT using deep learning Inproceedings

In: SPIE Medical Imaging, pp. 109490X, 2019.

Abstract | Links | BibTeX

4.

S. Bruns, J.M. Wolterink, R.W. van Hamersvelt, M. Zreik, T. Leiner, I. Išgum

Improving myocardium segmentation in cardiac CT angiography using spectral information Inproceedings

In: SPIE Medical Imaging, pp. 109492M, 2019.

Abstract | Links | BibTeX

5.

M. Zreik, R.W. van Hamersvelt, J.M. Wolterink, T. Leiner, M.A. Viergever, I. Išgum

Automatic Detection and Characterization of Coronary Artery Plaque and Stenosis using a Recurrent Convolutional Neural Network in Coronary CT Angiography Inproceedings

In: Medical Imaging with Deep Learning (MIDL 2018), 2018.

Abstract | Links | BibTeX

6.

M. Zreik; T. Leiner; B.D. de Vos; R.W. van Hamersvelt; M.A. Viergever; I. Isgum

Automatic segmentation of the left ventricle in cardiac CT angiography using convolutional neural networks Inproceedings

In: IEEE International Symposium on Biomedical Imaging, pp. pp. 40-43, 2016.

BibTeX

Abstracts

1.

R.W. van Hamersvelt, M. Zreik, M. Voskuil, I. Išgum, T. Leiner

Deep learning-based analysis of the left ventricular myocardium in coronary CTA images improves specificity for detection of functionally significant coronary artery stenosis Abstract

In: 2018.

Abstract | BibTeX

2.

R. van Hamersvelt, M. Zreik, N. Lessmann, J. Wolterink, M. Voskuil, M.A Viergever, T. Leiner, I. Isgum

Improving Specificity of Coronary CT Angiography for the Detection of Functionally Significant Coronary Artery Disease: A Deep Learning Approach Abstract

In: 2017.

Abstract | BibTeX

3.

M. Zreik, N. Lessmann, R. van Hamersvelt, J. Wolterink, M. Voskuil, M.A Viergever, T. Leiner, I. Isgum

Deep learning analysis of the left ventricular myocardium in cardiac CT images enables detection of functionally significant coronary artery stenosis regardless of coronary anatomy Abstract

In: 2017.

Abstract | BibTeX