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N. (Nadieh) Khalili PHD Candidate - OIO

  • Image Sciences Institute

N. Khalili

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Biography

Nadieh Khalili received her Bachelor of Science degree in Biomedical Engineering at Science & Research Branch of Azad University, Tehran, Iran. In 2015 she obtained her MSc magna cum laude in Biomedical Engineering at Bern University, Bern, 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 supervision of Dr. Ivana Isgum. Her research focuses on quantitative analysis of nutrition supplements on neonatal brain development. Nadieh´s field of interest lies primarily in medical image processing, machine learning, and deep learning.

Research Output (7)

Automatic brain tissue segmentation in fetal MRI using convolutional neural networks

Khalili N., Lessmann N., Turk E., Claessens N., de Heus R., Kolk T., Viergever M. A., Benders M. J. N. L., Isgum I. dec 2019, In: Magnetic Resonance Imaging. 64 , p. 77-89 13 p.

Brain and CSF Volumes in Fetuses and Neonates with Antenatal Diagnosis of Critical Congenital Heart Disease:A Longitudinal MRI Study

Claessens N H P, Khalili N, Isgum I, Ter Heide H, Steenhuis T J, Turk E, Jansen N J G, de Vries L S, Breur J M P J, de Heus R, Benders M J N L 28 mrt 2019, In: American Journal of Neuroradiology. 40 , p. 885-891 7 p.

Assessment of Brain Injury and Brain Volumes after Posthemorrhagic Ventricular Dilatation:A Nested Substudy of the Randomized Controlled ELVIS Trial

Cizmeci Mehmet N., Khalili Nadieh, Claessens Nathalie H. P., Groenendaal Floris, Liem Kian D., Heep Axel, Benavente-Fernandez Isabel, van Straaten Henrica L. M., van Wezel-Meijler Gerda, Steggerda Sylke J., Dudink Jeroen, Isgum Ivana, Whitelaw Andrew, Benders Manon J. N. L., de Vries Linda S., Han K., Woerdeman P., ter Horst H. J., Dijkman K. P., Ley D., Fellman V, de Haan T. R., Brouwer A. J., van't Verlaat E., Govaert P., Smit B. J., Agut Quijano T., Barcik U., Mathur A., Graca A. M. 13 mrt 2019, In: The Journal of Pediatrics. 208 , p. 191-197.e2

Automatic extraction of the intracranial volume in fetal and neonatal MR scans using convolutional neural networks

Khalili Nadieh, Turk E., Benders M. J.N.L., Moeskops P., Claessens N. H.P., de Heus R., Franx A., Wagenaar N., Breur J. M.P.J., Viergever M. A., Išgum I. 1 jan 2019, In: NeuroImage. Clinical. 24 , p. 102061 13 p.

Convolutional neural network-based regression for quantification of brain characteristics using MRI

Fernandes João, Alves Victor, Khalili Nadieh, Benders Manon J.N.L., Išgum Ivana, Pluim Josien, Moeskops Pim 1 jan 2019, p. 577-586 10 p.

Generative Adversarial Network for Segmentation of Motion Affected Neonatal Brain MRI

Khalili N., Turk E., Zreik M., Viergever M. A., Benders M. J.N.L., Išgum I. 1 jan 2019, p. 320-328 9 p.

Automatic segmentation of the intracranial volume in fetal MR images

Khalili N., Moeskops P., Claessens N. H.P., Scherpenzeel S., Turk E., de Heus R., Benders M. J.N.L., Viergever M. A., Pluim J. P.W., Išgum I. 2017, 10554 LNCS , p. 42-51 10 p.

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