The quantification of cerebrospinal fluid (CSF) in the human brain has shown to play an important role in early postnatal brain development. Extra-axial fluid (EA-CSF), which is characterized by CSF in the subarachnoid space, is a promising marker for the early detection of children at risk for neurodevelopmental disorders, such as Autism Spectrum Disorder (ASD). Yet, non-ventricular CSF quantification, in particular extra-axial CSF quantification, is not supported in the major neuro-imaging software solutions, such as FreeSurfer. Most current structural image analysis packages mask out the extra-axial CSF space in one of the first pre-processing steps. A quantitative protocol was previously developed by our group to objectively measure the volume of total EA-CSF volume using a pipeline workflow implemented in a series of python scripts. While this solution worked for our specific lab, a graphical user interface-based tool is necessary to facilitate the computation of extra-axial CSF volume across a wide array of neuroimaging studies and research labs. This paper presents the development of a novel open-source, cross-platform, user-friendly software tool, called Auto-EACSF, for the automatic computation of such extra-axial CSF volume. Auto-EACSF allows neuroimaging labs to quantify extra-axial CSF in their neuroimaging studies in order to investigate its role in normal and atypical brain 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.
Shape analysis is an important method used in neuroimaging research community due to its potential to precisely locate morphological changes between healthy and pathological structures. A popular shape analysis framework in the neuroimaging community is based on the encoding surface locations as spherical harmonics for a representation called SPHARM-PDM. The SPHARM-PDM pipeline takes a set of brain segmentation of a single brain structure (for example, hippocampus) as input and converts them into a corresponding spherical harmonic description (SPHARM), which is then sampled into triangulated surface (SPHARM-PDM). At present, the SPHARM-PDM pipeline utilizes an area-preserving optimization of the spherical mapping based on an initial heat-equation based mapping of the surface mesh to the unit sphere. In the case of objects with complex shape, this initial spherical mapping suffers from a high degree of mapping distortion that cannot always be corrected by the following optimization procedure. Here we proposed the use of an alternative initialization based on a conformal flattening. This method adopts a bijective angle preserving conformal flattening scheme to replace the heat equation mapping scheme as initialization for use in the SPHARM-PDM pipeline. After quantitative measures of shape calculated from various complex structures, we concluded that in most cases, the new pipeline produced dramatically better results than the old pipeline. The main contribution of this paper is a command line tool based on the Slicer Execution Model, which merges the conformal flattening into the SPHARM-PDM pipeline for use in the SALT shape analysis toolbox.
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.
Beatriz Paniagua, Sunghyung Kim, Mahmoud Moustapha, Martin Styner, Heather Cody-Hazlett, Rachel Gimple-Smith, Ashley Rumple, Joseph Piven, John Gilmore, Gary Skolnick, Kamlesh Patel
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.
KEYWORDS: Image segmentation, Shape analysis, Magnetic resonance imaging, Medical imaging, Statistical modeling, In vivo imaging, Magnetism, Brain, Statistical analysis, Principal component analysis, Data modeling, Neuroimaging, Image processing algorithms and systems
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.
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