28,286 research outputs found
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
SEAN: Image Synthesis with Semantic Region-Adaptive Normalization
We propose semantic region-adaptive normalization (SEAN), a simple but
effective building block for Generative Adversarial Networks conditioned on
segmentation masks that describe the semantic regions in the desired output
image. Using SEAN normalization, we can build a network architecture that can
control the style of each semantic region individually, e.g., we can specify
one style reference image per region. SEAN is better suited to encode,
transfer, and synthesize style than the best previous method in terms of
reconstruction quality, variability, and visual quality. We evaluate SEAN on
multiple datasets and report better quantitative metrics (e.g. FID, PSNR) than
the current state of the art. SEAN also pushes the frontier of interactive
image editing. We can interactively edit images by changing segmentation masks
or the style for any given region. We can also interpolate styles from two
reference images per region.Comment: Accepted as a CVPR 2020 oral paper. The interactive demo is available
at https://youtu.be/0Vbj9xFgoU
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Rewriting Human History and Empowering Indigenous Communities with Genome Editing Tools.
Appropriate empirical-based evidence and detailed theoretical considerations should be used for evolutionary explanations of phenotypic variation observed in the field of human population genetics (especially Indigenous populations). Investigators within the population genetics community frequently overlook the importance of these criteria when associating observed phenotypic variation with evolutionary explanations. A functional investigation of population-specific variation using cutting-edge genome editing tools has the potential to empower the population genetics community by holding "just-so" evolutionary explanations accountable. Here, we detail currently available precision genome editing tools and methods, with a particular emphasis on base editing, that can be applied to functionally investigate population-specific point mutations. We use the recent identification of thrifty mutations in the CREBRF gene as an example of the current dire need for an alliance between the fields of population genetics and genome editing
Global morphogenetic flow is accurately predicted by the spatial distribution of myosin motors.
During embryogenesis tissue layers undergo morphogenetic flow rearranging and folding into specific shapes. While developmental biology has identified key genes and local cellular processes, global coordination of tissue remodeling at the organ scale remains unclear. Here, we combine in toto light-sheet microscopy of the Drosophila embryo with quantitative analysis and physical modeling to relate cellular flow with the patterns of force generation during the gastrulation process. We find that the complex spatio-temporal flow pattern can be predicted from the measured meso-scale myosin density and anisotropy using a simple, effective viscous model of the tissue, achieving close to 90% accuracy with one time dependent and two constant parameters. Our analysis uncovers the importance of a) spatial modulation of myosin distribution on the scale of the embryo and b) the non-locality of its effect due to mechanical interaction of cells, demonstrating the need for the global perspective in the study of morphogenetic flow
High-efficiency texture coding and synthesis on point-based pear surface
© 2017 IOS Press and the authors. The fruit images on points cloud acquired by the current 3D scanner from field will appear a visible seams, inconvenient data acquisition or taking large space due to unorganized background. We give a SAOW method to cope with the space efficiency and realistic effects of texture synthesis on pear point model. At first, a point-quadtree is proposed to simplify the pear image division. Then, an adaptive multi-granularity morton coding scheme are presented to optimizing the memory space of pear image. At last, weighted oversampling mixing method is mainly focused on texture quality of pear surface. As shown in the experiment results, our adaptive division makes the memory space decline dramatically about 90.7% than non-division and 92.9% than general division respectively; adaptive code scheme helps to reduce the memory to 72.1% of ordinary morton code; weighted oversampling keeps the mixed texture more real and smoothly than current methods
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