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Our PUBLICATIONS

journal articles

4TOTAL 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
N. Khalili, N. Lessmann, E. Turk, N. Claessens, R. de Heus, T. Kolk, M. A. Viergever, M. J. N. L. Benders and I. Išgum, "Automatic brain tissue segmentation in fetal MRI using convolutional neural networks", Magnetic Resonance Imaging, 2019; 64: 77-89.
DOI | PMID
N. H. P. Claessens, N. Khalili, I. Išgum, H. ter Heide, T. J. Steenhuis, E. Turk, N. J. G. Jansen, L. S. de Vries, J. M. P. J. Breur, R. de Heus and M. J. N. L. Benders, "Brain and cerebrospinal fluid volumes in fetuses and neonates with antenatal diagnosis of critical congenital heart disease: a longitudinal MRI study", American Journal of Neuroradiology, 2019.
N. Khalili, E.Turk, M. J. N. L. Benders, P. Moeskops, N. H. P. Claessens, R. de Heuse, A. Franx, N. Wagenaar, J. M. P. J. Breur, M. A. Viergever and I. Išgum, "Automatic extraction of the intracranial volume in fetal and neonatal MR scans using convolutional neural networks", NeuroImage Clinical, 2019; 24 (102061).
DOI | PMID

inproceedings

4TOTAL 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. 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
J. Fernandes, V. Alves, N. Khalili, M. J. N. L. Benders, I. Išgum, J. Pluim and P. Moeskops, "Convolutional Neural Network-based regression for quantification of brain characteristics using MRI", WorldCist: 7th World Conference on Information Systems and Technologies, 2019; 931: 577-586.
URL
N. Khalili, P. Moeskops, N. H. P. Claessens, S. Scherpenzeel, E. Turk, R. de Heus, M. J. N. L. Benders, M. A. Viergever, J. P. W. Pluim and I. Išgum, "Automatic segmentation of the intracranial volume in fetal MR images", MICCAI Workshop on Fetal and InFant Image analysis (FIFI 2017), 2017.
URL

abstracts

2TOTAL RESOURCES
N. Khalili, N. Lessmann, E. Turk, M. A. Viergever, M. J. N. L. Benders and I. Išgum, "Brain tissue segmentation in fetal MRI using convolutional neural networks with simulated intensity inhomogeneities", International Society for Magnetic Resonance in Medicine, 27th Annual Meeting & Exhibition, 2019.
M.N. Cizmeci, N. Khalili, I. Išgum, N. Claessens, F. Groenendaal, D. Liem, A. Heep, I. B. Fernandez, I. van Straaten, G. van Wezel-Meijler, E. van ‘t Verlaat, A. Whitelaw, M.J.N.L. Benders, L.S. de Vries and ELVIS study group, "Timing of intervention for posthemorrhagic ventricular dilatation: effect on brain injury and brain volumes on term-equivalent age MRI", Pediatric Academic Societies (PAS) Meeting 2018, 2019.

phd theses

1TOTAL RESOURCES
N. Khalili, "Machine learning for automatic segmentation of neonatal and fetal MR brain images", Utrecht University, The Netherlands, 2020, ISBN: 978-90-393-7324-8.