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517 results on '"Viergever, M A"'

1. Automatic brain tissue segmentation in fetal MRI using convolutional neural networks

Author

Khalili, N., Lessmann, N., Turk, E., Claessens, N., de Heus, R., Kolk, T., Viergever, M. A., Benders, M. J. N. L., and Isgum, I.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
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., Comment: Published in Magnetic Resonance Imaging, 2019

Published
2019

2. Generative adversarial network for segmentation of motion affected neonatal brain MRI

Author

Khalili, N., Turk, E., Zreik, M., Viergever, M. A., Benders, M. J. N. L., and Isgum, I.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Automatic neonatal brain tissue segmentation in preterm born infants is a prerequisite for evaluation of brain development. However, automatic segmentation is often hampered by motion artifacts caused by infant head movements during image acquisition. Methods have been developed to remove or minimize these artifacts during image reconstruction using frequency domain data. However, frequency domain data might not always be available. Hence, in this study we propose a method for removing motion artifacts from the already reconstructed MR scans. The method employs a generative adversarial network trained with a cycle consistency loss to transform slices affected by motion into slices without motion artifacts, and vice versa. In the experiments 40 T2-weighted coronal MR scans of preterm born infants imaged at 30 weeks postmenstrual age were used. All images contained slices affected by motion artifacts hampering automatic tissue segmentation. To evaluate whether correction allows more accurate image segmentation, the images were segmented into 8 tissue classes: cerebellum, myelinated white matter, basal ganglia and thalami, ventricular cerebrospinal fluid, white matter, brain stem, cortical gray matter, and extracerebral cerebrospinal fluid. Images corrected for motion and corresponding segmentations were qualitatively evaluated using 5-point Likert scale. Before the correction of motion artifacts, median image quality and quality of corresponding automatic segmentations were assigned grade 2 (poor) and 3 (moderate), respectively. After correction of motion artifacts, both improved to grades 3 and 4, respectively. The results indicate that correction of motion artifacts in the image space using the proposed approach allows accurate segmentation of brain tissue classes in slices affected by motion artifacts., Comment: Accepted in Medical Image Computing and Computer Assisted Intervention 2019

Published
2019

3. Automatic segmentation of the intracranialvolume in fetal MR images

Author

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., and Išgum, I.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

MR images of the fetus allow non-invasive analysis of the fetal brain. Quantitative analysis of fetal brain development requires automatic brain tissue segmentation that is typically preceded by segmentation of the intracranial volume (ICV). This is challenging because fetal MR images visualize the whole moving fetus and in addition partially visualize the maternal body. This paper presents an automatic method for segmentation of the ICV in fetal MR images. The method employs a multi-scale convolutional neural network in 2D slices to enable learning spatial information from larger context as well as detailed local information. The method is developed and evaluated with 30 fetal T2-weighted MRI scans (average age $33.2\pm1.2$ weeks postmenstrual age). The set contains $10$ scans acquired in axial, $10$ in coronal and $10$ in sagittal imaging planes. A reference standard was defined in all images by manual annotation of the intracranial volume in $10$ equidistantly distributed slices. The automatic analysis was performed by training and testing the network using scans acquired in the representative imaging plane as well as combining the training data from all imaging planes. On average, the automatic method achieved Dice coefficients of 0.90 for the axial images, 0.90 for the coronal images and 0.92 for the sagittal images. Combining the training sets resulted in average Dice coefficients of 0.91 for the axial images, 0.95 for the coronal images, and 0.92 for the sagittal images. The results demonstrate that the evaluated method achieved good performance in extracting ICV in fetal MR scans regardless of the imaging plane.

Published
2017

4. Automatic Segmentation of the Intracranial Volume in Fetal MR Images

Author

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., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Cardoso, M. Jorge, editor, Arbel, Tal, editor, Melbourne, Andrew, editor, Bogunovic, Hrvoje, editor, Moeskops, Pim, editor, Chen, Xinjian, editor, Schwartz, Ernst, editor, Garvin, Mona, editor, Robinson, Emma, editor, Trucco, Emanuele, editor, Ebner, Michael, editor, Xu, Yanwu, editor, Makropoulos, Antonios, editor, Desjardin, Adrien, editor, and Vercauteren, Tom, editor

Published
2017
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7. Simulation of Nodules and Diffuse Infiltrates in Chest Radiographs Using CT Templates

Author

Litjens, G. J. S., Hogeweg, L., Schilham, A. M. R., de Jong, P. A., Viergever, M. A., van Ginneken, B., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Jiang, Tianzi, editor, Navab, Nassir, editor, Pluim, Josien P. W., editor, and Viergever, Max A., editor

Published
2010
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8. Registration Using Segment Intensity Remapping and Mutual Information

Author

Knops, Zeger F., Maintz, J. B. A., Viergever, M. A., Pluim, J. P. W., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Barillot, Christian, editor, Haynor, David R., editor, and Hellier, Pierre, editor

Published
2004
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13. Guide Eire Tracking During Endovascular Interventions

Author

Baert, S. A. M., Niessen, W. J., Meijering, E. H. W., Frangi, A. F., Viergever, M. A., Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Delp, Scott L., editor, DiGoia, Anthony M., editor, and Jaramaz, Branislav, editor

Published
2000
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17. Quantitation of Vessel Morphology from 3D MRA

Author

Frangi, A. F., Niessen, W. J., Hoogeveen, R. M., van Walsum, T., Viergever, M. A., Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Taylor, Chris, editor, and Colchester, Alain, editor

Published
1999
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21. In vivo analysis of trabecular bone architecture

Author

Niessen, W. J., López, A. M., van Enk, W. J., Roermund, P. M. v., Romeny, B. M. ter Haar, Viergever, M. A., Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Duncan, James, editor, and Gindi, Gene, editor

Published
1997
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28. elastix: a toolbox for intensity-based medical image registration

Author

Klein, S., Staring, M., Murphy, K., Viergever, M. A., and Pluim, J. P. W.

Subjects
Diagnostic imaging -- Analysis, Image processing -- Analysis, Public software -- Usage, Open source software, Business, Electronics, Electronics and electrical industries, Health care industry
Published
2010

44. Cortical Microinfarcts and White Matter Connectivity in Memory Clinic Patients

Author

Ferro, Doeschka, Heinen, Rutger, Robalo, Bruno de Brito, Kuijf, Hugo, Biessels, Geert Jan, Reijmer, Yael, van den Berg, E., Brundel, M., Bouvy, W. H., Exalto, L. G., Frijns, C. J. M., Groeneveld, O., Heringa, S. M., Kalsbeek, N., Kappelle, L. J., Reijmer, Y. D., Verwer, J., de Bresser, J., Leemans, A., Luijten, P. R., Viergever, M. A., Vincken, K. L., Zwanenburg, J. J. M., Koek, H. L., Hamaker, M., Faaij, R., Pleizier, M., Vriens, E., Neurology, Pediatrics, and Erasmus MC other

Subjects
medicine.medical_specialty, Neurology, Clinical Neurology, 030204 cardiovascular system & hematology, computer.software_genre, white matter connectivity, lcsh:RC346-429, White matter, 03 medical and health sciences, 0302 clinical medicine, Neuroimaging, Voxel, Cortex (anatomy), medicine, Effects of sleep deprivation on cognitive performance, vascular cognitive impairment, lcsh:Neurology. Diseases of the nervous system, Original Research, cerebral small vessel disease, business.industry, diffusion tensor imaging, microinfarcts, medicine.anatomical_structure, Neurology (clinical), business, Neuroscience, computer, 030217 neurology & neurosurgery, Diffusion MRI, Tractography
Abstract

Background and purpose: Cerebral microinfarcts (CMIs) are associated with cognitive impairment and dementia. CMIs might affect cognitive performance through disruption of cerebral networks. We investigated in memory clinic patients whether cortical CMIs are clustered in specific brain regions and if presence of cortical CMIs is associated with reduced white matter (WM) connectivity in tracts projecting to these regions.Methods: 164 memory clinic patients with vascular brain injury with a mean age of 72 +/- 11 years (54% male) were included. All underwent 3 tesla MRI, including a diffusion MRI and cognitive testing. Cortical CMIs were rated according to established criteria and their spatial location was marked. Diffusion imaging-based tractography was used to reconstruct WM connections and voxel based analysis (VBA) to assess integrity of WM directly below the cortex. WM connectivity and integrity were compared between patients with and without cortical CMIs for the whole brain and regions with a high CMI burden.Results: 30 patients (18%) had at least 1 cortical CMI [range 1-46]. More than 70% of the cortical CMIs were located in the superior frontal, middle frontal, and pre- and postcentral brain regions (covering 16% of the cortical surface). In these high CMI burden regions, presence of cortical CMIs was not associated with WM connectivity after correction for conventional neuroimaging markers of vascular injury. WM connectivity in the whole brain and WM voxels directly underneath the cortical surface did not differ between patients with and without cortical CMIs.Conclusion: Cortical CMIs displayed a strong local clustering in highly interconnected frontal, pre- and postcentral brain regions. Nevertheless, WM connections projecting to these regions were not disproportionally impaired in patients with compared to patients without cortical CMIs. Alternative mechanisms, such as focal disturbances in cortical structure and functioning, may better explain CMI associated cognitive impairment.

Published
2019
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