Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline

pre-printAutomated segmenting and labeling of individual brain anatomical regions, in MRI are challenging, due to the issue of individual structural variability. Although atlas-based segmentation has show its potential for both tissue and structure segmentation, due to the inherent natural variability as well as disease-related changes in MR appearance, a single atlas image is often inappropriate to represent the full population of datasets processed in a given neuroimaging study. As an alternative for the case of single atlas segmentation, the use of multiple altases alongside label fusion techniques has been introduced using a set of individual "atlases" that encompasses the expected variability in the studied population

Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline

Automated segmenting and labeling of individual brain anatomical regions, in MRI are challenging, due to the issue of individual structural variability. Although atlas-based segmentation has shown its potential for both tissue and structure segmentation, due to the inherent natural variability as well as disease-related changes in MR appearance, a single atlas image is often inappropriate to represent the full population of datasets processed in a given neuroimaging study. As an alternative for the case of single atlas segmentation, the use of multiple atlases alongside label fusion techniques has been introduced using a set of individual “atlases” that encompasses the expected variability in the studied population. In our study, we proposed a multi-atlas segmentation scheme with a novel graph-based atlas selection technique. We first paired and co-registered all atlases and the subject MR scans. A directed graph with edge weights based on intensity and shape similarity between all MR scans is then computed. The set of neighboring templates is selected via clustering of the graph. Finally, weighted majority voting is employed to create the final segmentation over the selected atlases. This multi-atlas segmentation scheme is used to extend a single-atlas-based segmentation toolkit entitled AutoSeg, which is an open-source, extensible C++ based software pipeline employing BatchMake for its pipeline scripting, developed at the Neuro Image Research and Analysis Laboratories of the University of North Carolina at Chapel Hill. AutoSeg performs N4 intensity inhomogeneity correction, rigid registration to a common template space, automated brain tissue classification based skull-stripping, and the multi-atlas segmentation. The multi-atlas-based AutoSeg has been evaluated on subcortical structure segmentation with a testing dataset of 20 adult brain MRI scans and 15 atlas MRI scans. The AutoSeg achieved mean Dice coefficients of 81.73% for the subcortical structures

A novel framework for the local extraction of extra-axial cerebrospinal fluid from MR brain images

The quantification of cerebrospinal fluid (CSF) in the human brain has shown to play an important role in early postnatal brain developmental. Extr a-axial fluid (EA-CSF), which is characterized by the CSF in the subarachnoid space, is promising in the early detection of children at risk for neurodevelopmental disorders. Currently, though, there is no tool to extract local EA-CSF measurements in a way that is suitable for localized analysis. In this paper, we propose a novel framework for the localized, cortical surface based analysis of EA-CSF. In our proposed processing, we combine probabilistic brain tissue segmentation, cortical surface reconstruction as well as streamline based local EA-CSF quantification. For streamline computation, we employ the vector field generated by solving a Laplacian partial differential equation (PDE) between the cortical surface and the outer CSF hull. To achieve sub-voxel accuracy while minimizing numerical errors, fourth-order Runge-Kutta (RK4) integration was used to generate the streamlines. Finally, the local EA-CSF is computed by integrating the CSF probability along the generated streamlines. The proposed local EA-CSF extraction tool was used to study the early postnatal brain development in typically developing infants. The results show that the proposed localized EA-CSF extraction pipeline can produce statistically significant regions that are not observed in previous global approach

Model selection for spatiotemporal modeling of early childhood sub-cortical development

Spatiotemporal shape models capture the dynamics of shape change over time and are an essential tool for monitoring and measuring anatomical growth or degeneration. In this paper we evaluate non-parametric shape regression on the challenging problem of modeling early childhood sub-cortical development starting from birth. Due to the flexibility of the model, it can be challenging to choose parameters which lead to a good model fit yet does not overfit. We systematically test a variety of parameter settings to evaluate model fit as well as the sensitivity of the method to specific parameters, and we explore the impact of missing data on model estimation

Heuristically improved Bayesian segmentation of brain MR images

One of the major tasks or even the most prevalent task in medical imageprocessing is image segmentation. Among them, brain MR images sufferfrom some difficulties such as intensity inhomogeneity of tissues, partialvolume effect, noise and some other imaging artifacts and so their segmentation is more challenging. Therefore, brain MRI segmentationbased on just gray values is prone to error. Hence involving problem specific heuristics and expert knowledge in designing segmentation algorithms seems to be useful. A two-phase segmentation algorithm basedon Bayesian method is proposed in this paper. The Bayesian part uses thegray value in segmenting images and the segmented image is used as theinput to the second phase to improve the misclassified pixels especially inborders between tissues. Similarity index is used to compare our algorithmwith the well known method of Ashburner which has been implemented inStatistical Parametric Mapping (SPM) package. Brainweb as a simulatedbrain MRI dataset is used in evaluating the proposed algorithm. Resultsshow that our algorithm performs well in comparison with the one implemented in SPM. It can be concluded that incorporating expert knowledge and problem specific heuristics improve segmentation result.The major advantage of proposed method is that one can update theknowledge base and incorporate new information into segmentationprocess by adding new rules.Keywords: Magnetic Resonance Imaging (MRI); Segmentation; Bayesianclassifier; Heuristic

Brain structure in sagittal craniosynostosis

Craniosynostosis, the premature fusion of one or more cranial sutures, leads to grossly abnormal head shapes and pressure elevations within the brain caused by these deformities. To date, accepted treatments for craniosynostosis involve improving surgical skull shape aesthetics. However, the relationship between improved head shape and brain structure after surgery has not been yet established. Typically, clinical standard care involves the collection of diagnostic medical computed tomography (CT) imaging to evaluate the fused sutures and plan the surgical treatment. CT is known to provide very good reconstructions of the hard tissues in the skull but it fails to acquire good soft brain tissue contrast. This study intends to use magnetic resonance imaging to evaluate brain structure in a small dataset of sagittal craniosynostosis patients and thus quantify the effects of surgical intervention in overall brain structure. Very importantly, these effects are to be contrasted with normative shape, volume and brain structure databases. The work presented here wants to address gaps in clinical knowledge in craniosynostosis focusing on understanding the changes in brain volume and shape secondary to surgery, and compare those with normally developing children. This initial pilot study has the potential to add significant quality to the surgical care of a vulnerable patient population in whom we currently have limited understanding of brain developmental outcomes

Automatic Extra-Axial Cerebrospinal Fluid

Automatic Extra-Axial Cerebrospinal Fluid (Auto EACSF) is an open-source, interactive tool for automatic computation of brain extra-axial cerebrospinal fluid (EA-CSF) in magnetic resonance image (MRI) scans of infants. Elevated extra-axial fluid volume is a possible biomarker for Autism Spectrum Disorder (ASD). Auto EACSF aims to automatically calculate the volume of EA-CSF and could therefore be used for early diagnosis of Autism. Auto EACSF is a user-friendly application that generates a Qt application to calculate the volume of EA-CSF. The application is run through a GUI, but also provides an advanced use mode that allows execution of different steps by themselves via Python and XML scripts.Bachelor of Scienc

A segmentation editing framework based on shape change statistics

Segmentation is a key task in medical image analysis because its accuracy significantly affects successive steps. Automatic segmentation methods often produce inadequate segmentations, which require the user to manually edit the produced segmentation slice by slice. Because editing is time-consuming, an editing tool that enables the user to produce accurate segmentations by only drawing a sparse set of contours would be needed. This paper describes such a framework as applied to a single object. Constrained by the additional information enabled by the manually segmented contours, the proposed framework utilizes object shape statistics to transform the failed automatic segmentation to a more accurate version. Instead of modeling the object shape, the proposed framework utilizes shape change statistics that were generated to capture the object deformation from the failed automatic segmentation to its corresponding correct segmentation. An optimization procedure was used to minimize an energy function that consists of two terms, an external contour match term and an internal shape change regularity term. The high accuracy of the proposed segmentation editing approach was confirmed by testing it on a simulated data set based on 10 in-vivo infant magnetic resonance brain data sets using four similarity metrics. Segmentation results indicated that our method can provide efficient and adequately accurate segmentations (Dice segmentation accuracy increase of 10%), with very sparse contours (only 10%), which is promising in greatly decreasing the work expected from the user

Intergenerational Effect of Maternal Exposure to Childhood Maltreatment on Newborn Brain Anatomy

Background Childhood maltreatment (CM) confers deleterious long-term consequences, and growing evidence suggests some of these effects may be transmitted across generations. We examined the intergenerational effect of maternal CM exposure on child brain structure and also addressed the hypothesis that this effect may start during the child's intrauterine period of life. Methods A prospective longitudinal study was conducted in a clinical convenience sample of 80 mother-child dyads. Maternal CM exposure was assessed using the Childhood Trauma Questionnaire. Structural magnetic resonance imaging was employed to characterize newborn global and regional brain (tissue) volumes near the time of birth. Results CM exposure was reported by 35% of the women. Maternal CM exposure was associated with lower child intracranial volume (F1,70 = 6.84, p =.011), which was primarily due to a global difference in cortical gray matter (F1,70 = 9.10, p =.004). The effect was independent of potential confounding variables, including maternal socioeconomic status, obstetric complications, obesity, recent interpersonal violence, pre- and early postpartum stress, gestational age at birth, infant sex, and postnatal age at magnetic resonance imaging scan. The observed group difference between offspring of CM-exposed mothers versus nonexposed mothers was 6%. Conclusions These findings represent the first report to date associating maternal CM exposure with variation in newborn brain structure. These observations support our hypothesis of intergenerational transmission of the effects of maternal CM exposure on child brain development and suggest this effect may originate during the child's intrauterine period of life, which may have downstream neurodevelopmental consequences

Neonatal hippocampal volume moderates the effects of early postnatal enrichment on cognitive development

Environmental enrichment, particularly during the early life phases of enhanced neuroplasticity, can stimulate cognitive development. However, individuals exhibit considerable variation in their response to environmental enrichment. Recent evidence suggests that certain neurophenotypes such as hippocampal size may index inter-individual differences in sensitivity to environmental conditions. We conducted a prospective, longitudinal investigation in a cohort of 75 mother-child dyads to investigate whether neonatal hippocampal volume moderates the effects of the postnatal environment on cognitive development. Newborn hippocampal volume was quantified shortly after birth (26.2 ± 12.5 days) by structural MRI. Measures of infant environmental enrichment (assessed by the IT-HOME) and cognitive state (assessed by the Bayley-III) were obtained at 6 months of age (6.09 ± 1.43 months). The interaction between neonatal hippocampal volume and enrichment predicted infant cognitive development (b = 0.01, 95 % CI [0.00, 0.02], t = 2.08, p =.04), suggesting that exposure to a stimulating environment had a larger beneficial effect on cognitive outcomes among infants with a larger hippocampus as neonates. Our findings suggest that the effects of the postnatal environment on infant cognitive development are conditioned, in part, upon characteristics of the newborn brain, and that newborn hippocampal volume is a candidate neurophenotype in this context