Nadieh Khalili
Image Sciences Institute, UMC Utrecht

Ph.D Candidate
e-mail: n [dot] khalili [at] umcutrecht [dot] nl
Phone: +31 88 75 50565
LinkedIn; Google Scholar 

Biography:

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

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.	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, I. Išgum Automatic extraction of the intracranial volume in fetal and neonatal MR scans using convolutional neural networks Journal Article NeuroImage Clinical, 24 (102061), 2019. Abstract | Links | BibTeX @article{Khalili2019d, title = {Automatic extraction of the intracranial volume in fetal and neonatal MR scans using convolutional neural networks}, author = {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, I. Išgum}, url = {https://www.sciencedirect.com/science/article/pii/S2213158219304085}, doi = {10.1016/j.nicl.2019.102061}, year = {2019}, date = {2019-10-26}, journal = {NeuroImage Clinical}, volume = {24}, number = {102061}, abstract = {MR images of infants and fetuses allow non-invasive analysis of the brain. Quantitative analysis of brain development requires automatic brain tissue segmentation that is typically preceded by segmentation of the intracranial volume (ICV). Fast changes in the size and morphology of the developing brain, motion artifacts, and large variation in the field of view make ICV segmentation a challenging task. We propose an automatic method for segmentation of the ICV in fetal and neonatal MRI scans. The method was developed and tested with a diverse set of scans regarding image acquisition parameters (i.e. field strength, image acquisition plane, image resolution), infant age (23–45 weeks post menstrual age), and pathology (posthaemorrhagic ventricular dilatation, stroke, asphyxia, and Down syndrome). The results demonstrate that the method achieves accurate segmentation with a Dice coefficient (DC) ranging from 0.98 to 0.99 in neonatal and fetal scans regardless of image acquisition parameters or patient characteristics. Hence, the algorithm provides a generic tool for segmentation of the ICV that may be used as a preprocessing step for brain tissue segmentation in fetal and neonatal brain MR scans.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close MR images of infants and fetuses allow non-invasive analysis of the brain. Quantitative analysis of brain development requires automatic brain tissue segmentation that is typically preceded by segmentation of the intracranial volume (ICV). Fast changes in the size and morphology of the developing brain, motion artifacts, and large variation in the field of view make ICV segmentation a challenging task. We propose an automatic method for segmentation of the ICV in fetal and neonatal MRI scans. The method was developed and tested with a diverse set of scans regarding image acquisition parameters (i.e. field strength, image acquisition plane, image resolution), infant age (23–45 weeks post menstrual age), and pathology (posthaemorrhagic ventricular dilatation, stroke, asphyxia, and Down syndrome). The results demonstrate that the method achieves accurate segmentation with a Dice coefficient (DC) ranging from 0.98 to 0.99 in neonatal and fetal scans regardless of image acquisition parameters or patient characteristics. Hence, the algorithm provides a generic tool for segmentation of the ICV that may be used as a preprocessing step for brain tissue segmentation in fetal and neonatal brain MR scans. Close https://www.sciencedirect.com/science/article/pii/S2213158219304085 doi:10.1016/j.nicl.2019.102061 Close
3.	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 Journal Article Magnetic Resonance Imaging, 64 , pp. 77-89, 2019. Abstract | Links | BibTeX @article{Khalili2019b, title = {Automatic brain tissue segmentation in fetal MRI using convolutional neural networks}, author = {N. Khalili, N. Lessmann, E. Turk, N. Claessens, R. de Heus, T. Kolk, M.A. Viergever, M.J.N.L. Benders, I. Išgum}, url = {https://doi.org/10.1016/j.mri.2019.05.020 https://arxiv.org/abs/1906.04713}, year = {2019}, date = {2019-06-07}, journal = {Magnetic Resonance Imaging}, volume = {64}, pages = {77-89}, abstract = {MR images of fetuses allow clinicians to detect brain abnormalities in an early stage of development. The cornerstone of volumetric and morphologic analysis in fetal MRI is segmentation of the fetal brain into different tissue classes. Manual segmentation is cumbersome and time consuming, hence automatic segmentation could substantially simplify the procedure. However, automatic brain tissue segmentation in these scans is challenging owing to artifacts including intensity inhomogeneity, caused in particular by spontaneous fetal movements during the scan. Unlike methods that estimate the bias field to remove intensity inhomogeneity as a preprocessing step to segmentation, we propose to perform segmentation using a convolutional neural network that exploits images with synthetically introduced intensity inhomogeneity as data augmentation. The method first uses a CNN to extract the intracranial volume. Thereafter, another CNN with the same architecture is employed to segment the extracted volume into seven brain tissue classes: cerebellum, basal ganglia and thalami, ventricular cerebrospinal fluid, white matter, brain stem, cortical gray matter and extracerebral cerebrospinal fluid. To make the method applicable to slices showing intensity inhomogeneity artifacts, the training data was augmented by applying a combination of linear gradients with random offsets and orientations to image slices without artifacts. To evaluate the performance of the method, Dice coefficient (DC) and Mean surface distance (MSD) per tissue class were computed between automatic and manual expert annotations. When the training data was enriched by simulated intensity inhomogeneity artifacts, the average achieved DC over all tissue classes and images increased from 0.77 to 0.88, and MSD decreased from 0.78 mm to 0.37 mm. These results demonstrate that the proposed approach can potentially replace or complement preprocessing steps, such as bias field corrections, and thereby improve the segmentation performance.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close MR images of fetuses allow clinicians to detect brain abnormalities in an early stage of development. The cornerstone of volumetric and morphologic analysis in fetal MRI is segmentation of the fetal brain into different tissue classes. Manual segmentation is cumbersome and time consuming, hence automatic segmentation could substantially simplify the procedure. However, automatic brain tissue segmentation in these scans is challenging owing to artifacts including intensity inhomogeneity, caused in particular by spontaneous fetal movements during the scan. Unlike methods that estimate the bias field to remove intensity inhomogeneity as a preprocessing step to segmentation, we propose to perform segmentation using a convolutional neural network that exploits images with synthetically introduced intensity inhomogeneity as data augmentation. The method first uses a CNN to extract the intracranial volume. Thereafter, another CNN with the same architecture is employed to segment the extracted volume into seven brain tissue classes: cerebellum, basal ganglia and thalami, ventricular cerebrospinal fluid, white matter, brain stem, cortical gray matter and extracerebral cerebrospinal fluid. To make the method applicable to slices showing intensity inhomogeneity artifacts, the training data was augmented by applying a combination of linear gradients with random offsets and orientations to image slices without artifacts. To evaluate the performance of the method, Dice coefficient (DC) and Mean surface distance (MSD) per tissue class were computed between automatic and manual expert annotations. When the training data was enriched by simulated intensity inhomogeneity artifacts, the average achieved DC over all tissue classes and images increased from 0.77 to 0.88, and MSD decreased from 0.78 mm to 0.37 mm. These results demonstrate that the proposed approach can potentially replace or complement preprocessing steps, such as bias field corrections, and thereby improve the segmentation performance. Close https://doi.org/10.1016/j.mri.2019.05.020 https://arxiv.org/abs/1906.04713 Close
4.	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 Article Journal of Pediatrics, 2019. Abstract | Links | BibTeX @article{Cizmeci2019, title = {Assessment of brain injury and brain volumes after posthemorrhagic ventricular dilatation: a nested substudy of the randomized controlled ELVIS trial}, author = {M.N. Cizmeci, N. Khalili, N.H.P. Claessens, F. Groenendaal, K.D. Liem}, url = {https://doi.org/10.1016/j.jpeds.2018.12.062}, year = {2019}, date = {2019-03-13}, journal = {Journal of Pediatrics}, abstract = {Objective To compare the effect of early and late intervention for posthemorrhagic ventricular dilatation on additional brain injury and ventricular volume using term-equivalent age-MRI. Study design In the Early vs Late Ventricular Intervention Study (ELVIS) trial, 126 preterm infants ≤34 weeks of gestation with posthemorrhagic ventricular dilatation were randomized to low-threshold (ventricular index >p97 and anterior horn width >6 mm) or high-threshold (ventricular index >p97 + 4 mm and anterior horn width >10 mm) groups. In 88 of those (80%) with a term-equivalent age-MRI, the Kidokoro Global Brain Abnormality Score and the frontal and occipital horn ratio were measured. Automatic segmentation was used for volumetric analysis. Results The total Kidokoro score of the infants in the low-threshold group (n = 44) was lower than in the high-threshold group (n = 44; median, 8 [IQR, 5-12] vs median 12 [IQR, 9-17], respectively; P < .001). More infants in the low-threshold group had a normal or mildly increased score vs more infants in the high-threshold group with a moderately or severely increased score (46% vs 11% and 89% vs 54%, respectively; P = .002). The frontal and occipital horn ratio was lower in the low-threshold group (median, 0.42 [IQR, 0.34-0.63]) than the high-threshold group (median 0.48 [IQR, 0.37-0.68], respectively; P = .001). Ventricular cerebrospinal fluid volumes could be calculated in 47 infants and were smaller in the low-threshold group (P = .03). Conclusions More brain injury and larger ventricular volumes were demonstrated in the high vs the low-threshold group. These results support the positive effects of early intervention for posthemorrhagic ventricular dilatation.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Objective To compare the effect of early and late intervention for posthemorrhagic ventricular dilatation on additional brain injury and ventricular volume using term-equivalent age-MRI. Study design In the Early vs Late Ventricular Intervention Study (ELVIS) trial, 126 preterm infants ≤34 weeks of gestation with posthemorrhagic ventricular dilatation were randomized to low-threshold (ventricular index >p97 and anterior horn width >6 mm) or high-threshold (ventricular index >p97 + 4 mm and anterior horn width >10 mm) groups. In 88 of those (80%) with a term-equivalent age-MRI, the Kidokoro Global Brain Abnormality Score and the frontal and occipital horn ratio were measured. Automatic segmentation was used for volumetric analysis. Results The total Kidokoro score of the infants in the low-threshold group (n = 44) was lower than in the high-threshold group (n = 44; median, 8 [IQR, 5-12] vs median 12 [IQR, 9-17], respectively; P < .001). More infants in the low-threshold group had a normal or mildly increased score vs more infants in the high-threshold group with a moderately or severely increased score (46% vs 11% and 89% vs 54%, respectively; P = .002). The frontal and occipital horn ratio was lower in the low-threshold group (median, 0.42 [IQR, 0.34-0.63]) than the high-threshold group (median 0.48 [IQR, 0.37-0.68], respectively; P = .001). Ventricular cerebrospinal fluid volumes could be calculated in 47 infants and were smaller in the low-threshold group (P = .03). Conclusions More brain injury and larger ventricular volumes were demonstrated in the high vs the low-threshold group. These results support the positive effects of early intervention for posthemorrhagic ventricular dilatation. Close https://doi.org/10.1016/j.jpeds.2018.12.062 Close
5.	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 Journal Article American Journal of Neuroradiology, 2019. Abstract | Links | BibTeX @article{Claessens2019, title = {Brain and cerebrospinal fluid volumes in fetuses and neonates with antenatal diagnosis of critical congenital heart disease: a longitudinal MRI study}, author = {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}, url = {http://www.ajnr.org/content/early/2019/03/28/ajnr.A6021.abstract}, year = {2019}, date = {2019-02-28}, journal = {American Journal of Neuroradiology}, abstract = {BACKGROUND AND PURPOSE: Fetuses and neonates with critical congenital heart disease are at risk of delayed brain development and neurodevelopmental impairments. Our aim was to investigate the association between fetal and neonatal brain volumes and neonatal brain injury in a longitudinally scanned cohort with an antenatal diagnosis of critical congenital heart disease and to relate fetal and neonatal brain volumes to postmenstrual age and type of congenital heart disease. MATERIALS AND METHODS: This was a prospective, longitudinal study including 61 neonates with critical congenital heart disease undergoing surgery with cardiopulmonary bypass <30 days after birth and MR imaging of the brain; antenatally (33 weeks postmenstrual age), neonatal preoperatively (first week), and postoperatively (7 days postoperatively). Twenty-six had 3 MR imaging scans; 61 had at least 1 fetal and/or neonatal MR imaging scan. Volumes (cubic centimeters) were calculated for total brain volume, unmyelinated white matter, cortical gray matter, cerebellum, extracerebral CSF, and ventricular CSF. MR images were reviewed for ischemic brain injury. RESULTS: Total fetal brain volume, cortical gray matter, and unmyelinated white matter positively correlated with preoperative neonatal total brain volume, cortical gray matter, and unmyelinated white matter (r = 0.5–0.58); fetal ventricular CSF and extracerebral CSF correlated with neonatal ventricular CSF and extracerebral CSF (r = 0.64 and 0.82). Fetal cortical gray matter, unmyelinated white matter, and the cerebellum were negatively correlated with neonatal ischemic injury (r = −0.46 to −0.41); fetal extracerebral CSF and ventricular CSF were positively correlated with neonatal ischemic injury (r = 0.40 and 0.23). Unmyelinated white matter:total brain volume ratio decreased with increasing postmenstrual age, with a parallel increase of cortical gray matter:total brain volume and cerebellum:total brain volume. Fetal ventricular CSF:intracranial volume and extracerebral CSF:intracranial volume ratios decreased with increasing postmenstrual age; however, neonatal ventricular CSF:intracranial volume and extracerebral CSF:intracranial volume ratios increased with postmenstrual age. CONCLUSIONS: This study reveals that fetal brain volumes relate to neonatal brain volumes in critical congenital heart disease, with a negative correlation between fetal brain volumes and neonatal ischemic injury. Fetal brain imaging has the potential to provide early neurologic biomarkers.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close BACKGROUND AND PURPOSE: Fetuses and neonates with critical congenital heart disease are at risk of delayed brain development and neurodevelopmental impairments. Our aim was to investigate the association between fetal and neonatal brain volumes and neonatal brain injury in a longitudinally scanned cohort with an antenatal diagnosis of critical congenital heart disease and to relate fetal and neonatal brain volumes to postmenstrual age and type of congenital heart disease. MATERIALS AND METHODS: This was a prospective, longitudinal study including 61 neonates with critical congenital heart disease undergoing surgery with cardiopulmonary bypass <30 days after birth and MR imaging of the brain; antenatally (33 weeks postmenstrual age), neonatal preoperatively (first week), and postoperatively (7 days postoperatively). Twenty-six had 3 MR imaging scans; 61 had at least 1 fetal and/or neonatal MR imaging scan. Volumes (cubic centimeters) were calculated for total brain volume, unmyelinated white matter, cortical gray matter, cerebellum, extracerebral CSF, and ventricular CSF. MR images were reviewed for ischemic brain injury. RESULTS: Total fetal brain volume, cortical gray matter, and unmyelinated white matter positively correlated with preoperative neonatal total brain volume, cortical gray matter, and unmyelinated white matter (r = 0.5–0.58); fetal ventricular CSF and extracerebral CSF correlated with neonatal ventricular CSF and extracerebral CSF (r = 0.64 and 0.82). Fetal cortical gray matter, unmyelinated white matter, and the cerebellum were negatively correlated with neonatal ischemic injury (r = −0.46 to −0.41); fetal extracerebral CSF and ventricular CSF were positively correlated with neonatal ischemic injury (r = 0.40 and 0.23). Unmyelinated white matter:total brain volume ratio decreased with increasing postmenstrual age, with a parallel increase of cortical gray matter:total brain volume and cerebellum:total brain volume. Fetal ventricular CSF:intracranial volume and extracerebral CSF:intracranial volume ratios decreased with increasing postmenstrual age; however, neonatal ventricular CSF:intracranial volume and extracerebral CSF:intracranial volume ratios increased with postmenstrual age. CONCLUSIONS: This study reveals that fetal brain volumes relate to neonatal brain volumes in critical congenital heart disease, with a negative correlation between fetal brain volumes and neonatal ischemic injury. Fetal brain imaging has the potential to provide early neurologic biomarkers. Close http://www.ajnr.org/content/early/2019/03/28/ajnr.A6021.abstract Close