Automatic segmentation of the preterm neonatal brain with MRI using supervised classification

Cortical folding ensues around 13-14 weeks gestational age and a qualitative analysis of the cortex around this period is required to observe and better understand the folds arousal. A quantitative assessment of cortical folding can be based on the cortical surface area, extracted from segmentations of unmyelinated white matter (UWM), cortical grey matter (CoGM) and cerebrospinal uid in the extracerebral space (CSF). This work presents a method for automatic segmentation of these tissue types in preterm infants. A set of T1- and T2-weighted images of ten infants scanned at 30 weeks postmenstrual age was used. The reference standard was obtained by manual expert segmentation. The method employs supervised pixel classification in three subsequent stages. The classification is performed based on the set of spatial and texture features. Segmentation results are evaluated in terms of Dice coefficient (DC), Hausdorff distance (HD), and modified Hausdorff distance (MHD) defined as 95th percentile of the HD. The method achieved average DC of 0.94 for UWM, 0.73 for CoGM and 0.86 for CSF. The average HD and MHD were 6.89 mm and 0.34 mm for UWM, 6.49 mm and 0.82 mm for CoGM, and 7.09 mm and 0.79 mm for CSF, respectively. The presented method can provide volumetric measurements of the segmented tissues, and it enables quantification of cortical characteristics. Therefore, the method provides a basis for evaluation of clinical relevance of these biomarkers in the given population. © 2013 SPIE

Automatic segmentation of the preterm neonatal brain with MRI using supervised classification

Cortical folding ensues around 13-14 weeks gestational age and a qualitative analysis of the cortex around this period is required to observe and better understand the folds arousal. A quantitative assessment of cortical folding can be based on the cortical surface area, extracted from segmentations of unmyelinated white matter (UWM), cortical grey matter (CoGM) and cerebrospinal uid in the extracerebral space (CSF). This work presents a method for automatic segmentation of these tissue types in preterm infants. A set of T1- and T2-weighted images of ten infants scanned at 30 weeks postmenstrual age was used. The reference standard was obtained by manual expert segmentation. The method employs supervised pixel classification in three subsequent stages. The classification is performed based on the set of spatial and texture features. Segmentation results are evaluated in terms of Dice coefficient (DC), Hausdorff distance (HD), and modified Hausdorff distance (MHD) defined as 95th percentile of the HD. The method achieved average DC of 0.94 for UWM, 0.73 for CoGM and 0.86 for CSF. The average HD and MHD were 6.89 mm and 0.34 mm for UWM, 6.49 mm and 0.82 mm for CoGM, and 7.09 mm and 0.79 mm for CSF, respectively. The presented method can provide volumetric measurements of the segmented tissues, and it enables quantification of cortical characteristics. Therefore, the method provides a basis for evaluation of clinical relevance of these biomarkers in the given population. © 2013 SPIE

Longitudinal Regional Brain Development and Clinical Risk Factors in Extremely Preterm Infants

OBJECTIVES: To investigate third-trimester extrauterine brain growth and correlate this with clinical risk factors in the neonatal period, using serially acquired brain tissue volumes in a large, unselected cohort of extremely preterm born infants. STUDY DESIGN: Preterm infants (gestational age <28 weeks) underwent brain magnetic resonance imaging (MRI) at around 30 weeks postmenstrual age and again around term equivalent age. MRIs were segmented in 50 different regions covering the entire brain. Multivariable regression analysis was used to determine the influence of clinical variables on volumes at both scans, as well as on volumetric growth. RESULTS: MRIs at term equivalent age were available for 210 infants and serial data were available for 131 infants. Growth over these 10 weeks was greatest for the cerebellum, with an increase of 258%. Sex, birth weight z-score, and prolonged mechanical ventilation showed global effects on brain volumes on both scans. The effect of brain injury on ventricular size was already visible at 30 weeks, whereas growth data and volumes at term-equivalent age revealed the effect of brain injury on the cerebellum. CONCLUSION: This study provides data about third-trimester extrauterine volumetric brain growth in preterm infants. Both global and local effects of several common clinical risk factors were found to influence serial volumetric measurements, highlighting the vulnerability of the human brain, especially in the presence of brain injury, during this period

Longitudinal Regional Brain Development and Clinical Risk Factors in Extremely Preterm Infants

OBJECTIVES: To investigate third-trimester extrauterine brain growth and correlate this with clinical risk factors in the neonatal period, using serially acquired brain tissue volumes in a large, unselected cohort of extremely preterm born infants. STUDY DESIGN: Preterm infants (gestational age <28 weeks) underwent brain magnetic resonance imaging (MRI) at around 30 weeks postmenstrual age and again around term equivalent age. MRIs were segmented in 50 different regions covering the entire brain. Multivariable regression analysis was used to determine the influence of clinical variables on volumes at both scans, as well as on volumetric growth. RESULTS: MRIs at term equivalent age were available for 210 infants and serial data were available for 131 infants. Growth over these 10 weeks was greatest for the cerebellum, with an increase of 258%. Sex, birth weight z-score, and prolonged mechanical ventilation showed global effects on brain volumes on both scans. The effect of brain injury on ventricular size was already visible at 30 weeks, whereas growth data and volumes at term-equivalent age revealed the effect of brain injury on the cerebellum. CONCLUSION: This study provides data about third-trimester extrauterine volumetric brain growth in preterm infants. Both global and local effects of several common clinical risk factors were found to influence serial volumetric measurements, highlighting the vulnerability of the human brain, especially in the presence of brain injury, during this period

Development of cortical morphology evaluated with longitudinal MR brain images of preterm infants

Introduction: 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). Methods: 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. Results: 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

MRI Based Preterm White Matter Injury Classification : The Importance of Sequential Imaging in Determining Severity of Injury

BACKGROUND: The evolution of non-hemorrhagic white matter injury (WMI) based on sequential magnetic resonance imaging (MRI) has not been well studied. Our aim was to describe sequential MRI findings in preterm infants with non-hemorrhagic WMI and to develop an MRI classification system for preterm WMI based on these findings. METHODS: Eighty-two preterm infants (gestation ≤35 weeks) were retrospectively included. WMI was diagnosed and classified based on sequential cranial ultrasound (cUS) and confirmed on MRI. RESULTS: 138 MRIs were obtained at three time-points: early (<2 weeks; n = 32), mid (2-6 weeks; n = 30) and term equivalent age (TEA; n = 76). 63 infants (77%) had 2 MRIs during the neonatal period. WMI was non-cystic in 35 and cystic in 47 infants. In infants with cystic-WMI early MRI showed extensive restricted diffusion abnormalities, cysts were already present in 3 infants; mid MRI showed focal or extensive cysts, without acute diffusion changes. A significant reduction in the size and/or extent of the cysts was observed in 32% of the infants between early/mid and TEA MRI. In 4/9 infants previously seen focal cysts were no longer identified at TEA. All infants with cystic WMI showed ≥2 additional findings at TEA: significant reduction in WM volume, mild-moderate irregular ventriculomegaly, several areas of increased signal intensity on T1-weighted-images, abnormal myelination of the PLIC, small thalami. CONCLUSION: In infants with extensive WM cysts at 2-6 weeks, cysts may be reduced in number or may even no longer be seen at TEA. A single MRI at TEA, without taking sequential cUS data and pre-TEA MRI findings into account, may underestimate the extent of WMI; based on these results we propose a new MRI classification for preterm non-hemorrhagic WMI

On development of functional brain connectivity in the young brain

Our brain is a complex network of structurally and functionally interconnected regions, shaped to efficiently process and integrate information. The development from a brain equipped with basic functionalities to an efficient network facilitating complex behavior starts during gestation and continues into adulthood. Resting-state functional MRI(rs-fMRI) enables the examination of developmental aspects of functional connectivity (FC) and functional brain networks. This review will discuss changes observed in the developing brain on the level of network FC from a gestational age of 20 weeks onwards. We discuss findings of resting-state fMRI studies showing that functional network development starts during gestation, creating a foundation for each of the resting-state networks (RSNs) to be established. Visual and sensorimotor areas are reported to develop first, with other networks, at different rates, increasing both in network connectivity and size over time. Reaching childhood, marked fine-tuning and specialization takes place in the regions necessary for higher-order cognitive functions

Anticoagulation Therapy and Imaging in Neonates With a Unilateral Thalamic Hemorrhage Due to Cerebral Sinovenous Thrombosis

Background and Purpose-Cerebral sinovenous thrombosis is a rare disorder with a high risk of an adverse neurodevelopmental outcome. Until now, anticoagulation therapy has been restricted to neonates without an associated parenchymal hemorrhage. In this study, we describe sequential neuroimaging findings and use of anticoagulation therapy in newborn infants with a unilateral thalamic hemorrhage due to cerebral sinovenous thrombosis. Methods-Ten neonates with a unilateral thalamic hemorrhage and cerebral sinovenous thrombosis were studied. Diagnosis was suspected using cranial ultrasound and confirmed with MRI/MR venography. Eight infants had a repeat MRI at 3 to 7 months. Neurodevelopmental outcome was assessed from 3 months until 5 years. Results-One infant died. Seven infants were treated with low-molecular-weight heparin. No side affects were noted. MRI showed involvement of multiple sinuses, additional intraventricular hemorrhage, and white matter lesions in all infants. Recanalization was present on the repeat MRI at 3 months in all infants. Treatment was delayed in one infant and anticoagulation was started only after extension of the thalamic hemorrhage. He required a ventriculoperitoneal drain for posthemorrhagic ventricular dilatation and developed cerebral visual impairment and global delay. Two other infants showed global delay and one of them also developed postneonatal epilepsy. Mild asymmetry in tone was present in 4 children. Conclusions-Cerebral sinovenous thrombosis was found in 10 neonates with unilateral thalamic hemorrhage. Diagnosis was suspected on cranial ultrasound and confirmed with MRI/MR venography. Treatment with low-molecular-weight heparin in newborn infants with a thalamic hemorrhage due to cerebral sinovenous thrombosis appears to be safe and should be considered. Long-term follow-up will be needed to assess cognitive outcome. (Stroke. 2009; 40: 2754-2760.

Automatic segmentation of MR brain images of preterm infants using supervised classification

\u3cp\u3ePreterm 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 stages 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 40weeks 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 30weeks 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 30weeks PMA, and 0.92 for WM, 0.80 for GM and 0.85 for CSF for the images acquired at 40weeks 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 30weeks, and 0.94 for WM, 0.76 for GM and 0.87 for CSF for the images acquired at 40weeks). 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.\u3c/p\u3