qurAI

Our PUBLICATIONS

journal articles

5TOTAL RESOURCES
M. Zreik, R. W. van Hamersvelt, N. Khalili, J. M. Wolterink, M. Voskuil, M. A. Viergever, T. Leiner and I. Išgum, "Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography", IEEE Transactions on Medical Imaging, 2020; 39 (5): 1545-1557.
DOI | PMID
M. Zreik, R. W. van Hamersvelt, J. M. Wolterink, T. Leiner, M. A. Viergever and I. Išgum, "A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography", IEEE Transactions on Medical Imaging, 2019; 38 (7): 1588-1598.
DOI | PMID
R. W. van Hamersvelt*, M. Zreik*, M. Voskuil, M. A. Viergever, I. Išgum and 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, 2019; 29 (5): 2350-2359.
DOI | PMID
N. Lessmann, B. van Ginneken, M. Zreik, P. A. de Jong, B. D. de Vos, M. A. Viergever and I. Išgum, "Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions", IEEE Transactions on Medical Imaging, 2018; 37 (2): 615-625.
DOI | PMID
M. Zreik, N. Lessmann, R. W. van Hamersvelt, J. M. Wolterink, M. Voskuil, M. A. Viergever, T. Leiner and 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; 44: 72-85.
DOI | PMID

inproceedings

6TOTAL RESOURCES
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", SPIE Medical Imaging, Image Processing, 2021; 11596: 115961F.
URL
N. Lessmann, J.M. Wolterink, M. Zreik, M.A. Viergever, B. van Ginneken, I. Išgum, "Vertebra partitioning with thin-plate spline surfaces steered by a convolutional neural network", Medical Imaging with Deep Learning (MIDL 2019), 2019.
URL
N. Khalili, E. Turk, M. Zreik, M. A. Viergever, M. J. N. L. Benders and I. Išgum, "Generative adversarial network for segmentation of motion affected neonatal brain MRI", Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Lecture Notes in Computer Science, 2019; 11766: 320-328.
URL
S. G. M. van Velzen, M. Zreik, N. Lessmann, M. A. Viergever, P. A. de Jong, H. M. Verkooijen and I. Išgum, "Direct prediction of cardiovascular mortality from low-dose chest CT using deep learning", SPIE Medical Imaging, Image Processing, 2019; 10949: 109490X.
URL
M. Zreik, R. W. van Hamersvelt, J. M. Wolterink, T. Leiner, M. A. Viergever and I. Išgum, "Automatic detection and characterization of coronary artery plaque and stenosis using a recurrent convolutional neural network in coronary CT angiography", Medical Imaging with Deep Learning (MIDL 2018), 2018.
M. Zreik, T. Leiner, B. D. de Vos, R. W. van Hamersvelt, M. A. Viergever and I. Isgum, "Automatic segmentation of the left ventricle in cardiac CT angiography using convolutional neural networks", IEEE International Symposium on Biomedical Imaging, 2016: pp. 40-43.

abstracts

3TOTAL RESOURCES
R. W. van Hamersvelt, M. Zreik, M. Voskuil, I. Išgum and 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.
M. Zreik, N. Lessmann, R. van Hamersvelt, J. Wolterink, M. Voskuil, M. A. Viergever, T. Leiner and 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.
R. van Hamersvelt, M. Zreik, N. Lessmann, J. Wolterink, M. Voskuil, M. A. Viergever, T. Leiner and 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.

phd theses

1TOTAL RESOURCES
M. Zreik, "Machine learning for coronary artery disease analysis in cardiac CT", Utrecht University, The Netherlands, 2020, ISBN: 978-94-6323-978-3.