748 research outputs found

    Neonatal brain tissue classification with morphological adaptation and unified segmentation

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    Measuring the distribution of brain tissue types (tissue classification) in neonates is necessary for studying typical and atypical brain development, such as that associated with preterm birth, and may provide biomarkers for neurodevelopmental outcomes. Compared with magnetic resonance images of adults, neonatal images present specific challenges that require the development of specialized, population-specific methods. This paper introduces MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation), which extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM) software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF), hippocampus and amygdala. We evaluated the performance of MANTiS using two independent datasets. The first dataset, provided by the NeoBrainS12 challenge, consisted of coronal T2-weighted images of preterm infants (born ≤30 weeks’ gestation) acquired at 30 weeks’ corrected gestational age (n= 5), coronal T2-weighted images of preterm infants acquired at 40 weeks’ corrected gestational age (n= 5) and axial T2-weighted images of preterm infants acquired at 40 weeks’ corrected gestational age (n= 5). The second dataset, provided by the Washington University NeuroDevelopmental Research (WUNDeR) group, consisted of T2-weighted images of preterm infants (born <30 weeks’ gestation) acquired shortly after birth (n= 12), preterm infants acquired at term-equivalent age (n= 12), and healthy term-born infants (born ≥38 weeks’ gestation) acquired within the first nine days of life (n= 12). For the NeoBrainS12 dataset, mean Dice scores comparing MANTiS with manual segmentations were all above 0.7, except for the cortical gray matter for coronal images acquired at 30 weeks. This demonstrates that MANTiS’ performance is competitive with existing techniques. For the WUNDeR dataset, mean Dice scores comparing MANTiS with manually edited segmentations demonstrated good agreement, where all scores were above 0.75, except for the hippocampus and amygdala. The results show that MANTiS is able to segment neonatal brain tissues well, even in images that have brain abnormalities common in preterm infants. MANTiS is available for download as an SPM toolbox from http://developmentalimagingmcri.github.io/mantis

    Infant’s MRI Brain Tissue Segmentation using Integrated CNN Feature Extractor and Random Forest

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    Infant MRI brain soft tissue segmentation become more difficult task compare with adult MRI brain tissue segmentation, due to Infant’s brain have a very low Signal to noise ratio among the white matter_WM and the gray matter _GM. Due the fast improvement of the overall brain at this time , the overall shape and appearance of the brain differs significantly. Manual segmentation of anomalous tissues is time-consuming and unpleasant. Essential Feature extraction in traditional machine algorithm is based on experts, required prior knowledge and also system sensitivity has change. Recently, bio-medical image segmentation based on deep learning has presented significant potential in becoming an important element of the clinical assessment process. Inspired by the mentioned objective, we introduce a methodology for analysing infant image in order to appropriately segment tissue of infant MRI images. In this paper, we integrated random forest classifier along with deep convolutional neural networks (CNN) for segmentation of infants MRI of Iseg 2017 dataset. We segmented infants MRI brain images into such as WM- white matter, GM-gray matter and CSF-cerebrospinal fluid tissues, the obtained result show that the recommended integrated CNN-RF method outperforms and archives a superior DSC-Dice similarity coefficient, MHD-Modified Hausdorff distance and ASD-Average surface distance for respective segmented tissue of infants brain MRI

    Segmentation of Infant Brain Using Nonnegative Matrix Factorization

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    This study develops an atlas-based automated framework for segmenting infants\u27 brains from magnetic resonance imaging (MRI). For the accurate segmentation of different structures of an infant\u27s brain at the isointense age (6-12 months), our framework integrates features of diffusion tensor imaging (DTI) (e.g., the fractional anisotropy (FA)). A brain diffusion tensor (DT) image and its region map are considered samples of a Markov-Gibbs random field (MGRF) that jointly models visual appearance, shape, and spatial homogeneity of a goal structure. The visual appearance is modeled with an empirical distribution of the probability of the DTI features, fused by their nonnegative matrix factorization (NMF) and allocation to data clusters. Projecting an initial high-dimensional feature space onto a low-dimensional space of the significant fused features with the NMF allows for better separation of the goal structure and its background. The cluster centers in the latter space are determined at the training stage by the K-means clustering. In order to adapt to large infant brain inhomogeneities and segment the brain images more accurately, appearance descriptors of both the first-order and second-order are taken into account in the fused NMF feature space. Additionally, a second-order MGRF model is used to describe the appearance based on the voxel intensities and their pairwise spatial dependencies. An adaptive shape prior that is spatially variant is constructed from a training set of co-aligned images, forming an atlas database. Moreover, the spatial homogeneity of the shape is described with a spatially uniform 3D MGRF of the second-order for region labels. In vivo experiments on nine infant datasets showed promising results in terms of the accuracy, which was computed using three metrics: the 95-percentile modified Hausdorff distance (MHD), the Dice similarity coefficient (DSC), and the absolute volume difference (AVD). Both the quantitative and visual assessments confirm that integrating the proposed NMF-fused DTI feature and intensity MGRF models of visual appearance, the adaptive shape prior, and the shape homogeneity MGRF model is promising in segmenting the infant brain DTI

    Association of prenatal exposure to early-life adversity with neonatal brain volumes at birth

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    Importance: Exposure to early-life adversity alters the structural development of key brain regions underlying neurodevelopmental impairments. The association between prenatal exposure to adversity and brain structure at birth remains poorly understood. Objective: To examine whether prenatal exposure to maternal social disadvantage and psychosocial stress is associated with neonatal global and regional brain volumes and cortical folding. Design, Setting, and Participants: This prospective, longitudinal cohort study included 399 mother-infant dyads of sociodemographically diverse mothers recruited in the first or early second trimester of pregnancy and their infants, who underwent brain magnetic resonance imaging in the first weeks of life. Mothers were recruited from local obstetric clinics in St Louis, Missouri from September 1, 2017, to February 28, 2020. Exposures: Maternal social disadvantage and psychosocial stress in pregnancy. Main Outcomes and Measures: Confirmatory factor analyses were used to create latent constructs of maternal social disadvantage (income-to-needs ratio, Area Deprivation Index, Healthy Eating Index, educational level, and insurance status) and psychosocial stress (Perceived Stress Scale, Edinburgh Postnatal Depression Scale, Everyday Discrimination Scale, and Stress and Adversity Inventory). Neonatal cortical and subcortical gray matter, white matter, cerebellum, hippocampus, and amygdala volumes were generated using semiautomated, age-specific, segmentation pipelines. Results: A total of 280 mothers (mean [SD] age, 29.1 [5.3] years; 170 [60.7%] Black or African American, 100 [35.7%] White, and 10 [3.6%] other race or ethnicity) and their healthy, term-born infants (149 [53.2%] male; mean [SD] infant gestational age, 38.6 [1.0] weeks) were included in the analysis. After covariate adjustment and multiple comparisons correction, greater social disadvantage was associated with reduced cortical gray matter (unstandardized β = -2.0; 95% CI, -3.5 to -0.5; P = .01), subcortical gray matter (unstandardized β = -0.4; 95% CI, -0.7 to -0.2; P = .003), and white matter (unstandardized β = -5.5; 95% CI, -7.8 to -3.3; P \u3c .001) volumes and cortical folding (unstandardized β = -0.03; 95% CI, -0.04 to -0.01; P \u3c .001). Psychosocial stress showed no association with brain metrics. Although social disadvantage accounted for an additional 2.3% of the variance of the left hippocampus (unstandardized β = -0.03; 95% CI, -0.05 to -0.01), 2.3% of the right hippocampus (unstandardized β = -0.03; 95% CI, -0.05 to -0.01), 3.1% of the left amygdala (unstandardized β = -0.02; 95% CI, -0.03 to -0.01), and 2.9% of the right amygdala (unstandardized β = -0.02; 95% CI, -0.03 to -0.01), no regional effects were found after accounting for total brain volume. Conclusions and Relevance: In this baseline assessment of an ongoing cohort study, prenatal social disadvantage was associated with global reductions in brain volumes and cortical folding at birth. No regional specificity for the hippocampus or amygdala was detected. Results highlight that associations between poverty and brain development begin in utero and are evident early in life. These findings emphasize that preventive interventions that support fetal brain development should address parental socioeconomic hardships

    Role of deep learning in infant brain MRI analysis

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    Deep learning algorithms and in particular convolutional networks have shown tremendous success in medical image analysis applications, though relatively few methods have been applied to infant MRI data due numerous inherent challenges such as inhomogenous tissue appearance across the image, considerable image intensity variability across the first year of life, and a low signal to noise setting. This paper presents methods addressing these challenges in two selected applications, specifically infant brain tissue segmentation at the isointense stage and presymptomatic disease prediction in neurodevelopmental disorders. Corresponding methods are reviewed and compared, and open issues are identified, namely low data size restrictions, class imbalance problems, and lack of interpretation of the resulting deep learning solutions. We discuss how existing solutions can be adapted to approach these issues as well as how generative models seem to be a particularly strong contender to address them

    Automated Morphometric Characterization of the Cerebral Cortex for the Developing and Ageing Brain

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    Morphometric characterisation of the cerebral cortex can provide information about patterns of brain development and ageing and may be relevant for diagnosis and estimation of the progression of diseases such as Alzheimer's, Huntington's, and schizophrenia. Therefore, understanding and describing the differences between populations in terms of structural volume, shape and thickness is of critical importance. Methodologically, due to data quality, presence of noise, PV effects, limited resolution and pathological variability, the automated, robust and time-consistent estimation of morphometric features is still an unsolved problem. This thesis focuses on the development of tools for robust cross-sectional and longitudinal morphometric characterisation of the human cerebral cortex. It describes techniques for tissue segmentation, structural and morphometric characterisation, cross-sectional and longitudinally cortical thickness estimation from serial MRI images in both adults and neonates. Two new probabilistic brain tissue segmentation techniques are introduced in order to accurately and robustly segment the brain of elderly and neonatal subjects, even in the presence of marked pathology. Two other algorithms based on the concept of multi-atlas segmentation propagation and fusion are also introduced in order to parcelate the brain into its multiple composing structures with the highest possible segmentation accuracy. Finally, we explore the use of the Khalimsky cubic complex framework for the extraction of topologically correct thickness measurements from probabilistic segmentations without explicit parametrisation of the edge. A longitudinal extension of this method is also proposed. The work presented in this thesis has been extensively validated on elderly and neonatal data from several scanners, sequences and protocols. The proposed algorithms have also been successfully applied to breast and heart MRI, neck and colon CT and also to small animal imaging. All the algorithms presented in this thesis are available as part of the open-source package NiftySeg
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