Role of deep learning in infant brain MRI analysis

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

Exercise Regulation of Marrow Adipose Tissue

Despite association with low bone density and skeletal fractures, marrow adipose tissue (MAT) remains poorly understood. The marrow adipocyte originates from the mesenchymal stem cell (MSC) pool that also gives rise to osteoblasts, chondrocytes, and myocytes, among other cell types. To date, the presence of MAT has been attributed to preferential biasing of MSC into the adipocyte rather than osteoblast lineage, thus negatively impacting bone formation. Here, we focus on understanding the physiology of MAT in the setting of exercise, dietary interventions, and pharmacologic agents that alter fat metabolism. The beneficial effect of exercise on musculoskeletal strength is known: exercise induces bone formation, encourages growth of skeletally supportive tissues, inhibits bone resorption, and alters skeletal architecture through direct and indirect effects on a multiplicity of cells involved in skeletal adaptation. MAT is less well studied due to the lack of reproducible quantification techniques. In recent work, osmium-based 3D quantification shows a robust response of MAT to both dietary and exercise intervention in that MAT is elevated in response to high-fat diet and can be suppressed following daily exercise. Exercise-induced bone formation correlates with suppression of MAT, such that exercise effects might be due to either calorie expenditure from this depot or from mechanical biasing of MSC lineage away from fat and toward bone, or a combination thereof. Following treatment with the anti-diabetes drug rosiglitazone – a PPARγ-agonist known to increase MAT and fracture risk – mice demonstrate a fivefold higher femur MAT volume compared to the controls. In addition to preventing MAT accumulation in control mice, exercise intervention significantly lowers MAT accumulation in rosiglitazone-treated mice. Importantly, exercise induction of trabecular bone volume is unhindered by rosiglitazone. Thus, despite rosiglitazone augmentation of MAT, exercise significantly suppresses MAT volume and induces bone formation. That exercise can both suppress MAT volume and increase bone quantity, notwithstanding the skeletal harm induced by rosiglitazone, underscores exercise as a powerful regulator of bone remodeling, encouraging marrow stem cells toward the osteogenic lineage to fulfill an adaptive need for bone formation. Thus, exercise represents an effective strategy to mitigate the deleterious effects of overeating and iatrogenic etiologies on bone and fat

FADTTSter: Accelerating hypothesis testing with functional analysis of diffusion tensor tract statistics

Functional Analysis of Diffusion Tensor Tract Statistics (FADTTS) is a toolbox for analysis of white matter (WM) fiber tracts. It allows associating diffusion properties along major WM bundles with a set of covariates of interest, such as age, diagnostic status and gender, and the structure of the variability of these WM tract properties. However, to use this toolbox, a user must have an intermediate knowledge in scripting languages (MATLAB). FADTTSter was created to overcome this issue and make the statistical analysis accessible to any non-technical researcher. FADTTSter is actively being used by researchers at the University of North Carolina. FADTTSter guides non-technical users through a series of steps including quality control of subjects and fibers in order to setup the necessary parameters to run FADTTS. Additionally, FADTTSter implements interactive charts for FADTTS' outputs. This interactive chart enhances the researcher experience and facilitates the analysis of the results. FADTTSter's motivation is to improve usability and provide a new analysis tool to the community that complements FADTTS. Ultimately, by enabling FADTTS to a broader audience, FADTTSter seeks to accelerate hypothesis testing in neuroimaging studies involving heterogeneous clinical data and diffusion tensor imaging. This work is submitted to the Biomedical Applications in Molecular, Structural, and Functional Imaging conference. The source code of this application is available in NITRC

CIVILITY: Cloud based interactive visualization of tractography brain connectome

Cloud based Interactive Visualization of Tractography Brain Connectome (CIVILITY) is an interactive visualization tool of brain connectome in the cloud. This application submits tasks to remote computing grids were the CIVILITY-tractography pipeline is deployed. The application will list the running tasks for the user and once a task is completed the brain connectome is visualized using Hierarchical Edge Bundling. The analysis pipeline uses FSL tools (bedpostx and probtrackx2) to generate a triangular matrix indicating the connectivity strength between different regions in the brain. This work is motivated by medical applications in which expensive computational tasks such as brain connectivity is needed and to provide a state of the art visualization tool of Brain Connectome. This work does not contribute any novelty with respect to the visualization methodology, is rather a new resource for the neuroimaging community. This work is submitted to the SPIE Biomedical Applications in Molecular, Structural, and Functional Imaging conference. The source code of this application is available in NITRC

Multi-object model-based multi-atlas segmentation for rodent brains using dense discrete correspondences

The delineation of rodent brain structures is challenging due to low-contrast multiple cortical and subcortical organs that are closely interfacing to each other. Atlas-based segmentation has been widely employed due to its ability to delineate multiple organs at the same time via image registration. The use of multiple atlases and subsequent label fusion techniques has further improved the robustness and accuracy of atlas-based segmentation. However, the accuracy of atlas-based segmentation is still prone to registration errors; for example, the segmentation of in vivo MR images can be less accurate and robust against image artifacts than the segmentation of post mortem images. In order to improve the accuracy and robustness of atlas-based segmentation, we propose a multi-object, model-based, multi-atlas segmentation method. We first establish spatial correspondences across atlases using a set of dense pseudo-landmark particles. We build a multi-object point distribution model using those particles in order to capture inter- and intra- subject variation among brain structures. The segmentation is obtained by fitting the model into a subject image, followed by label fusion process. Our result shows that the proposed method resulted in greater accuracy than comparable segmentation methods, including a widely used ANTs registration tool

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

Neonatal White Matter Maturation Is Associated With Infant Language Development

Background: While neonates have no sophisticated language skills, the neural basis for acquiring this function is assumed to already be present at birth. Receptive language is measurable by 6 months of age and meaningful speech production by 10-18 months of age. Fiber tracts supporting language processing include the corpus callosum (CC), which plays a key role in the hemispheric lateralization of language; the left arcuate fasciculus (AF), which is associated with syntactic processing; and the right AF, which plays a role in prosody and semantics. We examined if neonatal maturation of these fiber tracts is associated with receptive language development at 12 months of age. Methods: Diffusion-weighted imaging (DWI) was performed in 86 infants at 26.6 ± 12.2 days post-birth. Receptive language was assessed via the MacArthur-Bates Communicative Development Inventory at 12 months of age. Tract-based fractional anisotropy (FA) was determined using the NA-MIC atlas-based fiber analysis toolkit. Associations between neonatal regional FA, adjusted for gestational age at birth and age at scan, and language development at 12 months of age were tested using ANOVA models. Results: After multiple comparisons correction, higher neonatal FA was positively associated with receptive language at 12 months of age within the genu (p < 0.001), rostrum (p < 0.001), and tapetum (p < 0.001) of the CC and the left fronto-parietal AF (p = 0.008). No significant clusters were found in the right AF. Conclusion: Microstructural development of the CC and the AF in the newborn is associated with receptive language at 12 months of age, demonstrating that interindividual variation in white matter microstructure is relevant for later language development, and indicating that the neural foundation for language processing is laid well ahead of the majority of language acquisition. This suggests that some origins of impaired language development may lie in the intrauterine and potentially neonatal period of life. Understanding how interindividual differences in neonatal brain maturity relate to the acquisition of function, particularly during early development when the brain is in an unparalleled window of plasticity, is key to identifying opportunities for harnessing neuroplasticity in health and disease

Autotract: Automatic cleaning and tracking of fibers

We propose a new tool named Autotract to automate fiber tracking in diffusion tensor imaging (DTI). Autotract uses prior knowledge from a source DTI and a set of corresponding fiber bundles to extract new fibers for a target DTI. Autotract starts by aligning both DTIs and uses the source fibers as seed points to initialize a tractography algorithm. We enforce similarity between the propagated source fibers and automatically traced fibers by computing metrics such as fiber length and fiber distance between the bundles. By analyzing these metrics, individual fiber tracts can be pruned. As a result, we show that both bundles have similar characteristics. Additionally, we compare the automatically traced fibers against bundles previously generated and validated in the target DTI by an expert. This work is motivated by medical applications in which known bundles of fiber tracts in the human brain need to be analyzed for multiple datasets

Enhanced cortical thickness measurements for rodent brains via Lagrangian-based RK4 streamline computation

The cortical thickness of the mammalian brain is an important morphological characteristic that can be used to investigate and observe the brain's developmental changes that might be caused by biologically toxic substances such as ethanol or cocaine. Although various cortical thickness analysis methods have been proposed that are applicable for human brain and have developed into well-validated open-source software packages, cortical thickness analysis methods for rodent brains have not yet become as robust and accurate as those designed for human brains. Based on a previously proposed cortical thickness measurement pipeline for rodent brain analysis,1 we present an enhanced cortical thickness pipeline in terms of accuracy and anatomical consistency. First, we propose a Lagrangian-based computational approach in the thickness measurement step in order to minimize local truncation error using the fourth-order Runge-Kutta method. Second, by constructing a line object for each streamline of the thickness measurement, we can visualize the way the thickness is measured and achieve sub-voxel accuracy by performing geometric post-processing. Last, with emphasis on the importance of an anatomically consistent partial differential equation (PDE) boundary map, we propose an automatic PDE boundary map generation algorithm that is specific to rodent brain anatomy, which does not require manual labeling. The results show that the proposed cortical thickness pipeline can produce statistically significant regions that are not observed in the previous cortical thickness analysis pipeline

Regression uncertainty on the Grassmannian

Trends in longitudinal or cross-sectional studies over time are often captured through regression models. In their simplest manifestation, these regression models are formulated in ℝn. However, in the context of imaging studies, the objects of interest which are to be regressed are frequently best modeled as elements of a Riemannian manifold. Regression on such spaces can be accomplished through geodesic regression. This paper develops an approach to compute confidence intervals for geodesic regression models. The approach is general, but illustrated and specifically developed for the Grassmann manifold, which allows us, e.g., to regress shapes or linear dynamical systems. Extensions to other manifolds can be obtained in a similar manner. We demonstrate our approach for regression with 2D/3D shapes using synthetic and real data