@article{moesk18,
title = {Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI},
author = {P. Moeskops, J. de Bresser, H.J Kuijf, A.M Mendrik, G.J. Biessels, J.P. Pluim, I. Išgum},
url = {http://www.sciencedirect.com/science/article/pii/S2213158217302486},
year = {2017},
date = {2017-10-06},
journal = {NeuroImage Clinical},
volume = {17},
pages = {251-262},
abstract = {Automatic segmentation of brain tissues and white matter hyperintensities of presumed vascular origin (WMH) in MRI of older patients is widely described in the literature. Although brain abnormalities and motion artefacts are common in this age group, most segmentation methods are not evaluated in a setting that includes these items. In the present study, our tissue segmentation method for brain MRI was extended and evaluated for additional WMH segmentation. Furthermore, our method was evaluated in two large cohorts with a realistic variation in brain abnormalities and motion artefacts. The method uses a multi-scale convolutional neural network with a T1-weighted image, a T2-weighted fluid attenuated inversion recovery (FLAIR) image and a T1-weighted inversion recovery (IR) image as input. The method automatically segments white matter (WM), cortical grey matter (cGM), basal ganglia and thalami (BGT), cerebellum (CB), brain stem (BS), lateral ventricular cerebrospinal fluid (lvCSF), peripheral cerebrospinal fluid (pCSF), and WMH. Our method was evaluated quantitatively with images publicly available from the MRBrainS13 challenge (n = 20), quantitatively and qualitatively in relatively healthy older subjects (n = 96), and qualitatively in patients from a memory clinic (n = 110). The method can accurately segment WMH (Overall Dice coefficient in the MRBrainS13 data of 0.67) without compromising performance for tissue segmentations (Overall Dice coefficients in the MRBrainS13 data of 0.87 for WM, 0.85 for cGM, 0.82 for BGT, 0.93 for CB, 0.92 for BS, 0.93 for lvCSF, 0.76 for pCSF). Furthermore, the automatic WMH volumes showed a high correlation with manual WMH volumes (Spearman’s ρ = 0.83 for relatively healthy older subjects). In both cohorts, our method produced reliable segmentations (as determined by a human observer) in most images (relatively healthy/memory clinic: tissues 88%/77% reliable, WMH 85%/84% reliable) despite various degrees of brain abnormalities and motion artefacts. In conclusion, this study shows that a convolutional neural network-based segmentation method can accurately segment brain tissues and WMH in MR images of older patients with varying degrees of brain abnormalities and motion artefacts.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Moesk17,
title = {Prediction of cognitive and motor outcome of preterm infants based on automatic quantitative descriptors from neonatal MR brain images},
author = {P. Moeskops, I. Isgum, K. Keunen, N.H.P. Claessens, I.C. van Haastert, F. Groenendaal, L.S. de Vries, M.A. Viergever, M.J.N.L. Benders},
url = {https://www.nature.com/articles/s41598-017-02307-w},
year = {2017},
date = {2017-05-19},
journal = {Scientific Reports},
volume = {7},
number = {2163},
abstract = {This study investigates the predictive ability of automatic quantitative brain MRI descriptors for the identification of infants with low cognitive and/or motor outcome at 2–3 years chronological age. MR brain images of 173 patients were acquired at 30 weeks postmenstrual age (PMA) (n=86) and 40 weeks PMA (n=153) between 2008 and 2013. Eight tissue volumes and measures of cortical morphology were automatically computed. A support vector machine classifier was employed to identify infants who exhibit low cognitive and/or motor outcome (<85) at 2–3 years chronological age as assessed by the Bayley scales. Based on the images acquired at 30 weeks PMA, the automatic identification resulted in an area under the receiver operation characteristic curve (AUC) of 0.78 for low cognitive outcome, and an AUC of 0.80 for low motor outcome. Identification based on the change of the descriptors between 30 and 40 weeks PMA (n=66) resulted in an AUC of 0.80 for low cognitive outcome and an AUC of 0.85 for low motor outcome. This study provides evidence of the feasibility of identification of preterm infants at risk of cognitive and motor impairments based on descriptors automatically computed from images acquired at 30 and 40 weeks PMA.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Kers16,
title = {Relation between clinical risk factors, early cortical changes, and neurodevelopmental outcome in preterm infants},
author = {K.J. Kersbergen, F. Leroy, I. Isgum, F. Groenendaal, L.S. de Vries, N.H.P. Claessens, I.C. van Haastert, P. Moeskops, C. Fischer, J.-F. Mangin, M.A. Viergever, J. Dubois, M.J.N.L. Benders},
year = {2016},
date = {2016-07-06},
journal = {NeuroImage},
volume = {5},
number = {142},
pages = {301-310},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Clae16,
title = {Delayed cortical gray matter development in neonates with severe congenital heart disease},
author = {N.H.P. Claessens, P. Moeskops, A. Buchmann, B. Latal, W. Knirsch, I. Scheer, I. Isgum, L.S. de Vries, M.J.N.L. Benders, M. von Rhein},
year = {2016},
date = {2016-06-01},
journal = {Pediatric Research},
volume = {80},
number = {5},
pages = {668-674},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Moes16,
title = {Automatic segmentation of MR brain images with a convolutional neural network},
author = {P. Moeskops, M.A. Viergever, A.M. Mendrik, L.S. de Vries, M.J.N.L. Benders, I. Isgum},
year = {2016},
date = {2016-05-01},
journal = {IEEE Transactions on Medical Imaging},
volume = {35},
number = {5},
pages = {1252-1261},
abstract = {Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T2-weighted images of preterm infants acquired at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an average age of 70 years, and T1-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86, and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Moes15b,
title = {Development of cortical morphology evaluated with longitudinal MR brain images of preterm infants},
author = {P. Moeskops and M.J.N.L. Benders and K.J. Kersbergen and F. Groenendaal and L.S. de Vries and M.A. Viergever and I. Išgum},
year = {2015},
date = {2015-07-10},
journal = {PLOS ONE},
volume = {10},
number = {7},
pages = {e0131552},
abstract = {The cerebral cortex develops rapidly in the last trimester of pregnancy. In preterm infants, brain development is very vulnerable because of their often complicated extra-uterine conditions. The aim of this study was to quantitatively describe cortical development in a cohort of 85 preterm infants with and without brain injury imaged at 30 and 40 weeks postmenstrual age (PMA). In the acquired T2-weighted MR images, unmyelinated white matter (UWM), cortical grey matter (CoGM), and cerebrospinal fluid in the extracerebral space (CSF) were automatically segmented. Based on these segmentations, cortical descriptors evaluating volume, surface area, thickness, gyrification index, and global mean curvature were computed at both time points, for the whole brain, as well as for the frontal, temporal, parietal, and occipital lobes separately. Additionally, visual scoring of brain abnormality was performed using a conventional scoring system at 40 weeks PMA. The evaluated descriptors showed larger change in the occipital lobes than in the other lobes. Moreover, the cortical descriptors showed an association with the abnormality scores: gyrification index and global mean curvature decreased, whereas, interestingly, median cortical thickness increased with increasing abnormality score. This was more pronounced at 40 weeks PMA than at 30 weeks PMA, suggesting that the period between 30 and 40 weeks PMA might provide a window of opportunity for intervention to prevent delay in cortical development.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Moes15,
title = {Automatic segmentation of MR brain images of preterm infants using supervised classification},
author = {P. Moeskops, M.J.N.L. Benders, S.M. Chita, K.J. Kersbergen, F. Groenendaal, L.S. de Vries, M.A. Viergever, I. Išgum},
year = {2015},
date = {2015-06-07},
journal = {NeuroImage},
volume = {118},
pages = {628-641},
abstract = {Preterm birth is often associated with impaired brain development. The state and expected progression of preterm brain development can be evaluated using quantitative assessment of MR images. Such measurements require accurate segmentation of different tissue types in those images. This paper presents an algorithm for the automatic segmentation of unmyelinated white matter (WM), cortical grey matter (GM), and cerebrospinal fluid in the extracerebral space (CSF). The algorithm uses supervised voxel classification in three subsequent stages. In the first stage, voxels that can easily be assigned to one of the three tissue types are labelled. In the second stage, dedicated analysis of the remaining voxels is performed. The first and the second stage both use two-class classification for each tissue type separately. Possible inconsistencies that could result from these tissue-specific segmentation stages are resolved in the third stage, which performs multi-class classification. A set of T1- and T2-weighted images was analysed, but the optimised system performs automatic segmentation using a T2-weighted image only. We have investigated the performance of the algorithm when using training data randomly selected from completely annotated images as well as when using training data from only partially annotated images. The method was evaluated on images of preterm infants acquired at 30 and 40 weeks postmenstrual age (PMA). When the method was trained using random selection from the completely annotated images, the average Dice coefficients were 0.95 for WM, 0.81 for GM, and 0.89 for CSF on an independent set of images acquired at 30 weeks PMA. When the method was trained using only the partially annotated images, the average Dice coefficients were 0.95 for WM, 0.78 for GM and 0.87 for CSF for the images acquired at 30 weeks PMA, and 0.92 for WM, 0.80 for GM and 0.85 for CSF for the images acquired at 40 weeks PMA. Even though the segmentations obtained using training data from the partially annotated images resulted in slightly lower Dice coefficients, the performance in all experiments was close to that of a second human expert (0.93 for WM, 0.79 for GM and 0.86 for CSF for the images acquired at 30 weeks, and 0.94 for WM, 0.76 for GM and 0.87 for CSF for the images acquired at 40 weeks). These results show that the presented method is robust to age and acquisition protocol and that it performs accurate segmentation of WM, GM, and CSF when the training data is extracted from complete annotations as well as when the training data is extracted from partial annotations only. This extends the applicability of the method by reducing the time and effort necessary to create training data in a population with different characteristics.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Isgu14,
title = {Evaluation of automatic neonatal brain segmentation algorithms: the NeoBrainS12 challenge},
author = {I. Isgum, M.J.N.L. Benders, B. Avants, M.J. Cardoso, S.J. Counsell, E. Fischi Gomez, L. Gui, P. S Hüppi, K.J. Kersbergen, A. Makropoulos, A. Melbourne, P. Moeskops, C.P. Mol, M. Kuklisova-Murgasova, D. Rueckert, J.A. Schnabel, V. Srhoj-Egekher, J. Wu, S. Wang, L.S. de Vries, M.A. Viergever},
year = {2015},
date = {2015-01-01},
journal = {Medical Image Analysis},
volume = {20},
number = {1},
pages = {135-151},
abstract = {A number of algorithms for brain segmentation in preterm born infants have been published, but a reliable comparison of their performance is lacking. The NeoBrainS12 study (http://neobrains12.isi.uu.nl), providing three different image sets of preterm born infants, was set up to provide such a comparison. These sets are (i) axial scans acquired at 40 weeks corrected age, (ii) coronal scans acquired at 30 weeks corrected age and (iii) coronal scans acquired at 40 weeks corrected age. Each of these three sets consists of three T1- and T2-weighted MR images of the brain acquired with a 3T MRI scanner. The task was to segment cortical grey matter, non-myelinated and myelinated white matter, brainstem, basal ganglia and thalami, cerebellum, and cerebrospinal fluid in the ventricles and in the extracerebral space separately. Any team could upload the results and all segmentations were evaluated in the same way. This paper presents the results of eight participating teams. The results demonstrate that the participating methods were able to segment all tissue classes well, except myelinated white matter.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}