Majd Zreik
Image Sciences Institute, UMC Utrecht

PhD Candidate
E-mail: m [dot] zreik [at] umcutrecht [dot] nl
Phone: +31 88 75 50565
LinkedIn; Google Scholar

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 @article{Zreik2020b, title = {Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography}, author = {M. Zreik, R.W. van Hamersvelt, N. Khalili, J.M. Wolterink, M. Voskuil, M.A. Viergever, T. Leiner, I. Išgum}, url = {https://www.ncbi.nlm.nih.gov/pubmed/31725371}, doi = {10.1109/TMI.2019.2953054}, year = {2020}, date = {2020-05-01}, journal = {Transactions on Medical Imaging}, volume = {39}, number = {5}, pages = {1545-1557}, abstract = {In patients with obstructive coronary artery disease, the functional significance of a coronary artery stenosis needs to be determined to guide treatment. This is typically established through fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA). We present a method for automatic and non-invasive detection of patients requiring ICA, employing deep unsupervised analysis of complete coronary arteries in cardiac CT angiography (CCTA) images. We retrospectively collected CCTA scans of 187 patients, 137 of them underwent invasive FFR measurement in 192 different coronary arteries. These FFR measurements served as a reference standard for the functional significance of the coronary stenosis. The centerlines of the coronary arteries were extracted and used to reconstruct straightened multi-planar reformatted (MPR) volumes. To automatically identify arteries with functionally significant stenosis that require ICA, each MPR volume was encoded into a fixed number of encodings using two disjoint 3D and 1D convolutional autoencoders performing spatial and sequential encodings, respectively. Thereafter, these encodings were employed to classify arteries using a support vector machine classifier. The detection of coronary arteries requiring invasive evaluation, evaluated using repeated cross-validation experiments, resulted in an area under the receiver operating characteristic curve of 0.81±0.02 on the artery-level, and 0.87±0.02 on the patient-level. The results demonstrate the feasibility of automatic non-invasive detection of patients that require ICA and possibly subsequent coronary artery intervention. This could potentially reduce the number of patients that unnecessarily undergo ICA.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close In patients with obstructive coronary artery disease, the functional significance of a coronary artery stenosis needs to be determined to guide treatment. This is typically established through fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA). We present a method for automatic and non-invasive detection of patients requiring ICA, employing deep unsupervised analysis of complete coronary arteries in cardiac CT angiography (CCTA) images. We retrospectively collected CCTA scans of 187 patients, 137 of them underwent invasive FFR measurement in 192 different coronary arteries. These FFR measurements served as a reference standard for the functional significance of the coronary stenosis. The centerlines of the coronary arteries were extracted and used to reconstruct straightened multi-planar reformatted (MPR) volumes. To automatically identify arteries with functionally significant stenosis that require ICA, each MPR volume was encoded into a fixed number of encodings using two disjoint 3D and 1D convolutional autoencoders performing spatial and sequential encodings, respectively. Thereafter, these encodings were employed to classify arteries using a support vector machine classifier. The detection of coronary arteries requiring invasive evaluation, evaluated using repeated cross-validation experiments, resulted in an area under the receiver operating characteristic curve of 0.81±0.02 on the artery-level, and 0.87±0.02 on the patient-level. The results demonstrate the feasibility of automatic non-invasive detection of patients that require ICA and possibly subsequent coronary artery intervention. This could potentially reduce the number of patients that unnecessarily undergo ICA. Close https://www.ncbi.nlm.nih.gov/pubmed/31725371 doi:10.1109/TMI.2019.2953054 Close
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 @article{Zreik2018, title = {A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography}, author = {M. Zreik, R.W. van Hamersvelt, J.M. Wolterink, T. Leiner, M.A. Viergever, I. Išgum}, url = {https://ieeexplore.ieee.org/abstract/document/8550784 https://arxiv.org/abs/1804.04360}, year = {2018}, date = {2018-11-28}, journal = {IEEE Transactions on Medical Imaging}, volume = {38}, number = {7}, abstract = {Various types of atherosclerotic plaque and varying grades of stenosis could lead to different management of patients with coronary artery disease. Therefore, it is crucial to detect and classify the type of coronary artery plaque, as well as to detect and determine the degree of coronary artery stenosis. This study includes retrospectively collected clinically obtained coronary CT angiography (CCTA) scans of 163 patients. In these, the centerlines of the coronary arteries were extracted and used to reconstruct multi-planar reformatted (MPR) images for the coronary arteries. To define the reference standard, the presence and the type of plaque in the coronary arteries (no plaque, non-calcified, mixed, calcified), as well as the presence and the anatomical significance of coronary stenosis (no stenosis, nonsignificant i.e. < 50% luminal narrowing, significant i.e. ≥ 50% luminal narrowing) were manually annotated in the MPR images by identifying the start- and end-points of the segment of the artery affected by the plaque. To perform automatic analysis, a multi-task recurrent convolutional neural network is applied on coronary artery MPR images. First, a 3D convolutional neural network is utilized to extract features along the coronary artery. Subsequently, the extracted features are aggregated by a recurrent neural network that performs two simultaneous multiclass classification tasks. In the first task, the network detects and characterizes the type of the coronary artery plaque. In the second task, the network detects and determines the anatomical significance of the coronary artery stenosis. The network was trained and tested using CCTA images of 98 and 65 patients, respectively. For detection and characterization of coronary plaque, the method achieved an accuracy of 0.77. For detection of stenosis and determination of its anatomical significance, the method achieved an accuracy of 0.80. The results demonstrate that automatic detection and classification of coronary artery plaque and stenosis are feasible. This may enable automated triage of patients to those without coronary plaque and those with coronary plaque and stenosis in need for further cardiovascular workup.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Various types of atherosclerotic plaque and varying grades of stenosis could lead to different management of patients with coronary artery disease. Therefore, it is crucial to detect and classify the type of coronary artery plaque, as well as to detect and determine the degree of coronary artery stenosis. This study includes retrospectively collected clinically obtained coronary CT angiography (CCTA) scans of 163 patients. In these, the centerlines of the coronary arteries were extracted and used to reconstruct multi-planar reformatted (MPR) images for the coronary arteries. To define the reference standard, the presence and the type of plaque in the coronary arteries (no plaque, non-calcified, mixed, calcified), as well as the presence and the anatomical significance of coronary stenosis (no stenosis, nonsignificant i.e. < 50% luminal narrowing, significant i.e. ≥ 50% luminal narrowing) were manually annotated in the MPR images by identifying the start- and end-points of the segment of the artery affected by the plaque. To perform automatic analysis, a multi-task recurrent convolutional neural network is applied on coronary artery MPR images. First, a 3D convolutional neural network is utilized to extract features along the coronary artery. Subsequently, the extracted features are aggregated by a recurrent neural network that performs two simultaneous multiclass classification tasks. In the first task, the network detects and characterizes the type of the coronary artery plaque. In the second task, the network detects and determines the anatomical significance of the coronary artery stenosis. The network was trained and tested using CCTA images of 98 and 65 patients, respectively. For detection and characterization of coronary plaque, the method achieved an accuracy of 0.77. For detection of stenosis and determination of its anatomical significance, the method achieved an accuracy of 0.80. The results demonstrate that automatic detection and classification of coronary artery plaque and stenosis are feasible. This may enable automated triage of patients to those without coronary plaque and those with coronary plaque and stenosis in need for further cardiovascular workup. Close https://ieeexplore.ieee.org/abstract/document/8550784 https://arxiv.org/abs/1804.04360 Close
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 @article{vanHamersvelt*2018er, title = {Deep learning analysis of left ventricular myocardium in CT angiographic intermediate degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis }, author = {R.W. van Hamersvelt*, M. Zreik*, M. Voskuil, M.A. Viergever, I. Išgum, T. Leiner}, url = {https://link.springer.com/article/10.1007%2Fs00330-018-5822-3}, year = {2018}, date = {2018-10-01}, journal = {European Radiology}, volume = {29}, number = {5}, pages = {2350–2359}, abstract = {Objectives: To evaluate the added value of deep learning (DL) analysis of the left ventricular myocardium (LVM) in resting coronary CT angiography (CCTA) over determination of coronary degree of stenosis (DS), for identification of patients with functionally significant coronary artery stenosis. Methods: Patients who underwent CCTA prior to an invasive fractional flow reserve (FFR) measurement were retrospectively selected. Highest DS from CCTA was used to classify patients as having non-significant (≤ 24% DS), intermediate (25–69% DS), or significant stenosis (≥ 70% DS). Patients with intermediate stenosis were referred for fully automatic DL analysis of the LVM. The DL algorithm characterized the LVM, and likely encoded information regarding shape, texture, contrast enhancement, and more. Based on these encodings, features were extracted and patients classified as having a non-significant or significant stenosis. Diagnostic performance of the combined method was evaluated and compared to DS evaluation only. Functionally significant stenosis was defined as FFR ≤ 0.8 or presence of angiographic high-grade stenosis (≥ 90% DS). Results: The final study population consisted of 126 patients (77% male, 59 ± 9 years). Eighty-one patients (64%) had a functionally significant stenosis. The proposed method resulted in improved discrimination (AUC = 0.76) compared to classification based on DS only (AUC = 0.68). Sensitivity and specificity were 92.6% and 31.1% for DS only (≥ 50% indicating functionally significant stenosis), and 84.6% and 48.4% for the proposed method. Conclusion: The combination of DS with DL analysis of the LVM in intermediate-degree coronary stenosis may result in improved diagnostic performance for identification of patients with functionally significant coronary artery stenosis.}, note = {*equal contribution}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Objectives: To evaluate the added value of deep learning (DL) analysis of the left ventricular myocardium (LVM) in resting coronary CT angiography (CCTA) over determination of coronary degree of stenosis (DS), for identification of patients with functionally significant coronary artery stenosis. Methods: Patients who underwent CCTA prior to an invasive fractional flow reserve (FFR) measurement were retrospectively selected. Highest DS from CCTA was used to classify patients as having non-significant (≤ 24% DS), intermediate (25–69% DS), or significant stenosis (≥ 70% DS). Patients with intermediate stenosis were referred for fully automatic DL analysis of the LVM. The DL algorithm characterized the LVM, and likely encoded information regarding shape, texture, contrast enhancement, and more. Based on these encodings, features were extracted and patients classified as having a non-significant or significant stenosis. Diagnostic performance of the combined method was evaluated and compared to DS evaluation only. Functionally significant stenosis was defined as FFR ≤ 0.8 or presence of angiographic high-grade stenosis (≥ 90% DS). Results: The final study population consisted of 126 patients (77% male, 59 ± 9 years). Eighty-one patients (64%) had a functionally significant stenosis. The proposed method resulted in improved discrimination (AUC = 0.76) compared to classification based on DS only (AUC = 0.68). Sensitivity and specificity were 92.6% and 31.1% for DS only (≥ 50% indicating functionally significant stenosis), and 84.6% and 48.4% for the proposed method. Conclusion: The combination of DS with DL analysis of the LVM in intermediate-degree coronary stenosis may result in improved diagnostic performance for identification of patients with functionally significant coronary artery stenosis. Close https://link.springer.com/article/10.1007%2Fs00330-018-5822-3 Close
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 @article{Zreik2017b, title = {Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis}, author = {M. Zreik, N. Lessmann, R.W. van Hamersvelt, J.M. Wolterink, M. Voskuil, M.A. Viergever, T. Leiner, I. Išgum}, url = {https://arxiv.org/abs/1711.08917}, year = {2018}, date = {2018-02-01}, journal = {Medical Image Analysis}, volume = {44}, pages = {72-85}, abstract = {In patients with coronary artery stenoses of intermediate severity, the functional signicance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical practice. To reduce the number of ICA procedures, we present a method for automatic identication of patients with functionally signicant coronary artery stenoses, employing deep learning analysis of the left ventricle (LV) myocardium in rest coronary CT angiography (CCTA). The study includes consecutively acquired CCTA scans of 166 patients who underwent invasive FFR measurements. To identify patients with a functionally signicant coronary artery stenosis, analysis is performed in several stages. First, the LV myocardium is segmented using a multiscale convolutional neural network (CNN). To characterize the segmented LV myocardium, it is subsequently encoded using unsupervised convolutional autoencoder (CAE). As ischemic changes are expected to appear locally, the LV myocardium is divided into a number of spatially connected clusters, and statistics of the encodings are computed as features. Thereafter, patients are classied according to the presence of functionally signicant stenosis using an SVM classier based on the extracted features. Quantitative evaluation of LV myocardium segmentation in 20 images resulted in an average Dice coecient of 0:91 and an average mean absolute distance between the segmented and reference LV boundaries of 0:7 mm. Twenty CCTA images were used to train the LV myocardium encoder. Classication of patients was evaluated in the remaining 126 CCTA scans in 50 10-fold cross-validation experiments and resulted in an area under the receiver operating characteristic curve of 0.74  0.02. At sensitivity levels 0.60, 0.70 and 0.80, the corresponding specicity was 0.77, 0.71 and 0.59, respectively. The results demonstrate that automatic analysis of the LV myocardium in a single CCTA scan acquired at rest, without assessment of the anatomy of the coronary arteries, can be used to identify patients with functionally signicant coronary artery stenosis. This might reduce the number of patients undergoing unnecessary invasive FFR measurements.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close In patients with coronary artery stenoses of intermediate severity, the functional signicance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical practice. To reduce the number of ICA procedures, we present a method for automatic identication of patients with functionally signicant coronary artery stenoses, employing deep learning analysis of the left ventricle (LV) myocardium in rest coronary CT angiography (CCTA). The study includes consecutively acquired CCTA scans of 166 patients who underwent invasive FFR measurements. To identify patients with a functionally signicant coronary artery stenosis, analysis is performed in several stages. First, the LV myocardium is segmented using a multiscale convolutional neural network (CNN). To characterize the segmented LV myocardium, it is subsequently encoded using unsupervised convolutional autoencoder (CAE). As ischemic changes are expected to appear locally, the LV myocardium is divided into a number of spatially connected clusters, and statistics of the encodings are computed as features. Thereafter, patients are classied according to the presence of functionally signicant stenosis using an SVM classier based on the extracted features. Quantitative evaluation of LV myocardium segmentation in 20 images resulted in an average Dice coecient of 0:91 and an average mean absolute distance between the segmented and reference LV boundaries of 0:7 mm. Twenty CCTA images were used to train the LV myocardium encoder. Classication of patients was evaluated in the remaining 126 CCTA scans in 50 10-fold cross-validation experiments and resulted in an area under the receiver operating characteristic curve of 0.74  0.02. At sensitivity levels 0.60, 0.70 and 0.80, the corresponding specicity was 0.77, 0.71 and 0.59, respectively. The results demonstrate that automatic analysis of the LV myocardium in a single CCTA scan acquired at rest, without assessment of the anatomy of the coronary arteries, can be used to identify patients with functionally signicant coronary artery stenosis. This might reduce the number of patients undergoing unnecessary invasive FFR measurements. Close https://arxiv.org/abs/1711.08917 Close
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 @article{Lessmann2017, title = {Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions}, author = {N. Lessmann, B. van Ginneken, M. Zreik, P.A. de Jong, B.D. de Vos, M.A. Viergever, I. Išgum}, url = {https://arxiv.org/pdf/1711.00349.pdf}, year = {2018}, date = {2018-02-01}, journal = {IEEE Transactions on Medical Imaging}, volume = {37}, number = {2}, pages = {615-625}, abstract = {Heavy smokers undergoing screening with low-dose chest CT are affected by cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in screening enable quantification of atherosclerotic calcifications and thus enable identification of subjects at increased cardiovascular risk. This paper presents a method for automatic detection of coronary artery, thoracic aorta and cardiac valve calcifications in low-dose chest CT using two consecutive convolutional neural networks. The first network identifies and labels potential calcifications according to their anatomical location and the second network identifies true calcifications among the detected candidates. This method was trained and evaluated on a set of 1744 CT scans from the National Lung Screening Trial. To determine whether any reconstruction or only images reconstructed with soft tissue filters can be used for calcification detection, we evaluated the method on soft and medium/sharp filter reconstructions separately. On soft filter reconstructions, the method achieved F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta, aortic valve and mitral valve calcifications, respectively. On sharp filter reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively. Linearly weighted kappa coefficients for risk category assignment based on per subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter reconstructions, respectively. These results demonstrate that the presented method enables reliable automatic cardiovascular risk assessment in all low-dose chest CT scans acquired for lung cancer screening.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Heavy smokers undergoing screening with low-dose chest CT are affected by cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in screening enable quantification of atherosclerotic calcifications and thus enable identification of subjects at increased cardiovascular risk. This paper presents a method for automatic detection of coronary artery, thoracic aorta and cardiac valve calcifications in low-dose chest CT using two consecutive convolutional neural networks. The first network identifies and labels potential calcifications according to their anatomical location and the second network identifies true calcifications among the detected candidates. This method was trained and evaluated on a set of 1744 CT scans from the National Lung Screening Trial. To determine whether any reconstruction or only images reconstructed with soft tissue filters can be used for calcification detection, we evaluated the method on soft and medium/sharp filter reconstructions separately. On soft filter reconstructions, the method achieved F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta, aortic valve and mitral valve calcifications, respectively. On sharp filter reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively. Linearly weighted kappa coefficients for risk category assignment based on per subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter reconstructions, respectively. These results demonstrate that the presented method enables reliable automatic cardiovascular risk assessment in all low-dose chest CT scans acquired for lung cancer screening. Close https://arxiv.org/pdf/1711.00349.pdf Close