372 research outputs found
Testing Foundations of Biological Scaling Theory Using Automated Measurements of Vascular Networks
Scientists have long sought to understand how vascular networks supply blood
and oxygen to cells throughout the body. Recent work focuses on principles that
constrain how vessel size changes through branching generations from the aorta
to capillaries and uses scaling exponents to quantify these changes. Prominent
scaling theories predict that combinations of these exponents explain how
metabolic, growth, and other biological rates vary with body size.
Nevertheless, direct measurements of individual vessel segments have been
limited because existing techniques for measuring vasculature are invasive,
time consuming, and technically difficult. We developed software that extracts
the length, radius, and connectivity of in vivo vessels from contrast-enhanced
3D Magnetic Resonance Angiography. Using data from 20 human subjects, we
calculated scaling exponents by four methods--two derived from local properties
of branching junctions and two from whole-network properties. Although these
methods are often used interchangeably in the literature, we do not find
general agreement between these methods, particularly for vessel lengths.
Measurements for length of vessels also diverge from theoretical values, but
those for radius show stronger agreement. Our results demonstrate that vascular
network models cannot ignore certain complexities of real vascular systems and
indicate the need to discover new principles regarding vessel lengths
Semiautomated Skeletonization of the Pulmonary Arterial Tree in Micro-CT Images
We present a simple and robust approach that utilizes planar images at different angular rotations combined with unfiltered back-projection to locate the central axes of the pulmonary arterial tree. Three-dimensional points are selected interactively by the user. The computer calculates a sub- volume unfiltered back-projection orthogonal to the vector connecting the two points and centered on the first point. Because more x-rays are absorbed at the thickest portion of the vessel, in the unfiltered back-projection, the darkest pixel is assumed to be the center of the vessel. The computer replaces this point with the newly computer-calculated point. A second back-projection is calculated around the original point orthogonal to a vector connecting the newly-calculated first point and user-determined second point. The darkest pixel within the reconstruction is determined. The computer then replaces the second point with the XYZ coordinates of the darkest pixel within this second reconstruction. Following a vector based on a moving average of previously determined 3- dimensional points along the vessel\u27s axis, the computer continues this skeletonization process until stopped by the user. The computer estimates the vessel diameter along the set of previously determined points using a method similar to the full width-half max algorithm. On all subsequent vessels, the process works the same way except that at each point, distances between the current point and all previously determined points along different vessels are determined. If the difference is less than the previously estimated diameter, the vessels are assumed to branch. This user/computer interaction continues until the vascular tree has been skeletonized
Automation Process for Morphometric Analysis of Volumetric CT Data from Pulmonary Vasculature in Rats
With advances in medical imaging scanners, it has become commonplace to generate large multidimensional datasets. These datasets require tools for a rapid, thorough analysis. To address this need, we have developed an automated algorithm for morphometric analysis incorporating A Visualization Workshop computational and image processing libraries for three-dimensional segmentation, vascular tree generation and structural hierarchical ordering with a two-stage numeric optimization procedure for estimating vessel diameters. We combine this new technique with our mathematical models of pulmonary vascular morphology to quantify structural and functional attributes of lung arterial trees. Our physiological studies require repeated measurements of vascular structure to determine differences in vessel biomechanical properties between animal models of pulmonary disease. Automation provides many advantages including significantly improved speed and minimized operator interaction and biasing. The results are validated by comparison with previously published rat pulmonary arterial micro-CT data analysis techniques, in which vessels were manually mapped and measured using intense operator intervention
Intensity-Based Skeletonization of CryoEM Gray-Scale Images Using a True Segmentation-Free Algorithm
Cryo-electron microscopy is an experimental technique that is able to produce 3D gray-scale images of protein molecules. In contrast to other experimental techniques, cryo-electron microscopy is capable of visualizing large molecular complexes such as viruses and ribosomes. At medium resolution, the positions of the atoms are not visible and the process cannot proceed. The medium-resolution images produced by cryo-electron microscopy are used to derive the atomic structure of the proteins in de novo modeling. The skeletons of the 3D gray-scale images are used to interpret important information that is helpful in de novo modeling. Unfortunately, not all features of the image can be captured using a single segmentation. In this paper, we present a segmentation-free approach to extract the gray-scale curve-like skeletons. The approach relies on a novel representation of the 3D image, where the image is modeled as a graph and a set of volume trees. A test containing 36 synthesized maps and one authentic map shows that our approach can improve the performance of the two tested tools used in de novo modeling. The improvements were 62 and 13 percent for Gorgon and DP-TOSS, respectively
Hieroglyph: Hierarchical Glia Graph Skeletonization and Matching
Automatic 3D reconstruction of glia morphology is a powerful tool necessary
for investigating the role of microglia in neurological disorders in the
central nervous system. Current glia skeleton reconstruction techniques fail to
capture an accurate tracing of the processes over time, useful for the study of
the microglia motility and morphology in the brain during healthy and diseased
states. We propose Hieroglyph, a fully automatic temporal 3D skeleton
reconstruction algorithm for glia imaged via 3D multiphoton microscopy.
Hieroglyph yielded a 21% performance increase compared to state of the art
automatic skeleton reconstruction methods and outperforms the state of the art
in different measures of consistency on datasets of 3D images of microglia. The
results from this method provide a 3D graph and digital reconstruction of glia
useful for a myriad of morphological analyses that could impact studies in
brain immunology and disease.Comment: submitted to IEEE International Conference on Image Processing, 201
BRONCO: Automated modelling of the bronchovascular bundle using the Computed Tomography Images
Segmentation of the bronchovascular bundle within the lung parenchyma is a
key step for the proper analysis and planning of many pulmonary diseases. It
might also be considered the preprocessing step when the goal is to segment the
nodules from the lung parenchyma. We propose a segmentation pipeline for the
bronchovascular bundle based on the Computed Tomography images, returning
either binary or labelled masks of vessels and bronchi situated in the lung
parenchyma. The method consists of two modules, modeling of the bronchial tree
and vessels. The core revolves around a similar pipeline, the determination of
the initial perimeter by the GMM method, skeletonization, and hierarchical
analysis of the created graph. We tested our method on both low-dose CT and
standard-dose CT, with various pathologies, reconstructed with various slice
thicknesses, and acquired from various machines. We conclude that the method is
invariant with respect to the origin and parameters of the CT series. Our
pipeline is best suited for studies with healthy patients, patients with lung
nodules, and patients with emphysema
Customizable tubular model for n-furcating blood vessels and its application to 3D reconstruction of the cerebrovascular system
Understanding the 3D cerebral vascular network is one of the pressing issues impacting the diagnostics of various systemic disorders and is helpful in clinical therapeutic strategies. Unfortunately, the existing software in the radiological workstation does not meet the expectations of radiologists who require a computerized system for detailed, quantitative analysis of the human cerebrovascular system in 3D and a standardized geometric description of its components. In this study, we show a method that uses 3D image data from magnetic resonance imaging with contrast to create a geometrical reconstruction of the vessels and a parametric description of the reconstructed segments of the vessels. First, the method isolates the vascular system using controlled morphological growing and performs skeleton extraction and optimization. Then, around the optimized skeleton branches, it creates tubular objects optimized for quality and accuracy of matching with the originally isolated vascular data. Finally, it optimizes the joints on n-furcating vessel segments. As a result, the algorithm gives a complete description of shape, position in space, position relative to other segments, and other anatomical structures of each cerebrovascular system segment. Our method is highly customizable and in principle allows reconstructing vascular structures from any 2D or 3D data. The algorithm solves shortcomings of currently available methods including failures to reconstruct the vessel mesh in the proximity of junctions and is free of mesh collisions in high curvature vessels. It also introduces a number of optimizations in the vessel skeletonization leading to a more smooth and more accurate model of the vessel network. We have tested the method on 20 datasets from the public magnetic resonance angiography image database and show that the method allows for repeatable and robust segmentation of the vessel network and allows to compute vascular lateralization indices. Graphical abstract: [Figure not available: see fulltext.]</p
Geometry-Driven Detection, Tracking and Visual Analysis of Viscous and Gravitational Fingers
Viscous and gravitational flow instabilities cause a displacement front to
break up into finger-like fluids. The detection and evolutionary analysis of
these fingering instabilities are critical in multiple scientific disciplines
such as fluid mechanics and hydrogeology. However, previous detection methods
of the viscous and gravitational fingers are based on density thresholding,
which provides limited geometric information of the fingers. The geometric
structures of fingers and their evolution are important yet little studied in
the literature. In this work, we explore the geometric detection and evolution
of the fingers in detail to elucidate the dynamics of the instability. We
propose a ridge voxel detection method to guide the extraction of finger cores
from three-dimensional (3D) scalar fields. After skeletonizing finger cores
into skeletons, we design a spanning tree based approach to capture how fingers
branch spatially from the finger skeletons. Finally, we devise a novel
geometric-glyph augmented tracking graph to study how the fingers and their
branches grow, merge, and split over time. Feedback from earth scientists
demonstrates the usefulness of our approach to performing spatio-temporal
geometric analyses of fingers.Comment: Published at IEEE Transactions on Visualization and Computer Graphic
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