I was born, raised, and studied my bachelor in Tehran, Iran. I needed more of an adventure when I deceided to continue my master's studies in Biomedical Engineering at Bern University, Switzerland. My master's studies were focusing on medical image analysis. After having some courses in machine learning and computer vision, I decided to follow this path in my Ph.D. In 2016, I joined the Image Sciences Institute as a Ph.D. candidate. My research was focusing on image analysis of neonates and fetuses using machine learning and deep learning. The results of my research were published in world-class journals and conferences and are currently using in clinical research. I am passionate about artificial intelligence and its application to improve our life quality. Here you can find my CV.
Selected Journal Publication
N. Khalili, N. van der Aa, F. Groenendaal, L. de Vries, J. Dudink, M. Zreik, N. Wagenaar,
J. Breur, M. Viergever, and M. B. I. Išgum. “Brain tissue segmentation in neonatal MRI
using multi-modal CNN”, in preparation (2020).
M. Zreik, N. Hampe, T. Leiner, N. Khalili, J. Wolterink, M. Voskuil, M. Viergever, and
I. Išgum. “Combined analysis of coronary arteries and the lef ventricular myocardium
in cardiac CT angiography for detection of patients with functionally signifcant stenosis”, Submitted (2020).
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, vol. 39 (2020), pp. 1545–1557.
N. Khalili, E. Turk, M. Benders, P. Moeskops, N. Claessens, R. de Heus, A. Franx, N.
Wagenaar, J. Breur, M. Viergever, and I. Išgum. “Automatic extraction of the intracranial volume in fetal and neonatal MR scans using convolutional neural networks”, NeuroImage: Clinical, vol. 24 (2019), p. 102061.
N. Khalili, N. Lessmann, E. Turk, N. Claessens, R. de Heus, T. Kolk, M. A. Viergever,
M. J. Benders, and I. Išgum. “Automatic brain tissue segmentation in fetal MRI using
convolutional neural networks”, Magnetic Resonance Imaging, vol. 64 (2019), pp. 77–89.
N. Claessens, N. Khalili, I. Išgum, H. Ter Heide, T. Steenhuis, E. Turk, N. Jansen, L.
de Vries, J. Breur, R. de Heus, and M. Benders. “Brain and CSF volumes in fetuses and
neonates with antenatal diagnosis of critical congenital heart disease: a longitudinal
MRI study”, American Journal of Neuroradiology, vol. 40 (2019), pp. 885–891.
M. N. Cizmeci, N. Khalili, N. H. Claessens, F. Groenendaal, K. D. Liem, A. Heep, I.
Benavente-Fernández, H. L. van Straaten, G. van Wezel-Meijler, S. J. Steggerda, J. Dudink,
I. Išgum, A. Whitelaw, M. J. Benders, L. de Vries, and the ELVIS study group. “Assessment of brain injury and brain volumes afer posthemorrhagic ventricular dilatation:
162 Publications a nested substudy of the randomized controlled elvis trial”, The Journal of Pediatrics,
vol. 208 (2019), pp. 191–197.
Conferences
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, Cham:
Springer International Publishing, 2019, pp. 320–328. Selected for oral presentation (top 5%)
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N. Khalili, N. Lessmann, E. Turk, N. Claessens, R. de Heus, T. Kolk, M. Viergever, M.
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, 2019. Received cum laude poster award, invited talk.
J. Fernandes, V. Alves, N. Khalili, M. J. Benders, I. Išgum, J. Pluim, and P. Moeskops.
“Convolutional neural network-based regression for quantifcation of brain characteristics using MRI”, World Conference on Information Systems and Technologies, Springer. 2019, pp. 577–586.
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”, Fetal, Infant and OphthalmicMedical Image Analysis, Cham: Springer International Publishing, 2017, pp. 42–51.