Ph.D Candidate
Phone: +31 88 75 50565


Nadieh Khalili received her Bachelor of Science degree in Biomedical Engineering at Science & Research University, Tehran, Iran. In 2015 she obtained her MSc magna cum laude in Biomedical Engineering at Bern University, Switzerland. Her Master thesis entitled ”Multi-modal registration of 2D histology images on 3D CT dataset”.

In 2016, Nadieh joined to Image Science Institute as a Ph.D. candidate under the supervision of Dr. Ivana Isgum. Her Ph.D focuses on developing novel deep learning methods for quantitative analysis of neonate and fetal brain MRI.

Current projects:

Quantitative analysis of nutrition supplements on neonatal brain development

Papers in international journals

1.N. Khalili, N. Lessmann, E. Turk, N. Claessens, R. de Heus, T. Kolk, M.A. Viergever, M.J.N.L. Benders, I. Išgum. Automatic brain tissue segmentation in fetal MRI using convolutional neural networks. Magnetic Resonance Imaging (in press), 2019 Abstract | pdf )
2.M.N. Cizmeci, N. Khalili, N.H.P. Claessens, F. Groenendaal, K.D. Liem. Assessment of brain injury and brain volumes after posthemorrhagic ventricular dilatation: a nested substudy of the randomized controlled ELVIS trial. Journal of Pediatrics, 2019 Abstract | pdf )
3.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, 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 Abstract | pdf )

Papers in conference proceedings

1.N. Khalili, E. Turk, M. Zreik, M.A. Viergever, M.J.N.L. Benders, I. Išgum. Generative adversarial network for segmentation of motion affected neonatal brain MRI. In: Medical Image Computing and Computer Assisted Intervention − MICCAI 2019: 22nd International Conference, Shenzhen, China, in press , 2019 ( pdf )
2.J. Fernandes, V. Alves, N. Khalili3, M.J.N.L. Benders, I. Išgum, J. Pluim, P. Moeskops. Convolutional Neural Network-based regression for quantification of brain characteristics using MRI. In: WorldCist: 7th World Conference on Information Systems and Technologies , 2019, pp. 577-586 Abstract | pdf )
3.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, I. Išgum. Automatic segmentation of the intracranial volume in fetal MR images. In: MICCAI Workshop on Fetal and InFant Image analysis (FIFI 2017), 2017 Abstract | pdf )


1.N. Khalili, N. Lessmann, E. Turk, M.A. Viergever, M.J.N.L. Benders, 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 Abstract )
2.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, the 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 Abstract )