PhD Candidate
E-mail: m.zreik@umcutrecht.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.


Papers in international journals

1.M. Zreik, R.W. van Hamersvelt, J.M. Wolterink, T. Leiner, M.A. Viergever, I. Isgum. A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography. IEEE Transactions on Medical Imaging, in press, 2019 Abstract | pdf )
2.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 . European Radiology, in press, 2018, (*equal contribution) ( pdf )
3.M. Zreik, N. Lessmann, R. 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. Medical Image Analysis, 2018, vol. 44, pp. 72-85 Abstract | pdf )
4.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. IEEE Transactions on Medical Imaging, 2018, vol. 37, nr. 2, pp. 615-625 Abstract | pdf )

Papers in conference proceedings

1.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. In: SPIE Medical Imaging, 2019 Abstract | pdf )
2.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. In: SPIE Medical Imaging, 2019 Abstract | pdf )
3.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. In: Medical Imaging with Deep Learning (MIDL 2018), 2018 Abstract | pdf )
4.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. In: IEEE International Symposium on Biomedical Imaging, 2016, pp. pp. 40-43

Abstracts

1.R. W. van Hamersvelt, M. Zreik, M. Voskuil, I. Isgum, 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. European Congress of Radiology (ECR), 2018 Abstract )
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. Radiological Society of North America, 103rd Annual Meeting, 2017 Abstract )
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. Radiological Society of North America, 103rd Annual Meeting, 2017 Abstract )