Stripe Parameterization of Tubular Surfaces

We present a novel algorithm for automatic parameterization of tube-like surfaces of arbitrary genus such as the surfaces of knots, trees, blood vessels, neurons, or any tubular graph with a globally consistent stripe texture. Mathematically these surfaces can be described as thickened graphs, and the calculated parameterization stripe will follow either around the tube, along the underlying graph, a spiraling combination of both, or obey an arbitrary texture map whose charts have a 180 degree symmetry. We use the principal curvature frame field of the underlying tube-like surface to guide the creation of a global, topologically consistent stripe parameterization of the surface. Our algorithm extends the QuadCover algorithm and is based, first, on the use of so-called projective vector fields instead of frame fields, and second, on different types of branch points. That does not only simplify the mathematical theory, but also reduces computation time by the decomposition of the underlying stiffness matrices

Weighted Labels for 3D Image Segmentation

Segmentation tools in medical imaging are either based on editing geometric curves or on the assignment of region labels to image voxels. While the first approach is well suited to describe smooth contours at subvoxel accuracy, the second approach is conceptually more simple and guarantees a unique classification of image areas. However, contours extracted from labeled images typically exhibit strong staircase artifacts and are not well suited to represent smooth tissue boundaries. In this paper we describe how this drawback can be circumvented by supplementing region labels with additional weights. We integrated our approach into an interactive segmentation system providing a well-defined set of manual and semi-automatic editing tools. All tools update both region labels as well as the corresponding weights simultaneously, thus allowing one to define segmentation results at high resolution. We applied our techniques to generate 3D polygonal models of anatomical structures

A Statistical Shape Model for the Liver

Abstract. The use of statistical shape models is a promising approach for robust segmentation of medical images. One of the major challenges in building a 3D shape model from a training set of segmented instances of an object is the determination of the correspondence between them. We propose a novel geometric approach that is based on minimizing the distortion of the mapping between two surfaces. In this work we investigate the accuracy and completeness of a 3D statistical shape model for the liver built from 20 manually segmented individual CT data sets. The quality of the shape model is crucial for its application as a segmentation tool.

Use it or lose it: measuring trends in wild species subject to substantial use

The unsustainable use of wild animals and plants is thought to be a significant driver of biodiversity loss in many regions of the world. The international community has therefore called for action to ensure the sustainable use of living resources and safeguard them for future generations. Indicators that can track changes in populations of species used by humans are essential tools for measuring progress towards these ideals and informing management decisions. Here we present two indicators that could be used to track changes in populations of utilized vertebrate species and levels of harvest sustainability. Preliminary results based on sample data both at the global level and for the Arctic show that utilized species are faring better than other species overall. This could be a consequence of better management of these populations, as indicated by more sustainable harvest levels in recent decades. Limitations of the indicators are still apparent; in particular, there is a lack of data on harvested populations of some vertebrate classes and from certain regions. Focusing monitoring efforts on broadening the scope of data collected and identifying interactions with other potential drivers of decline will strengthen these indicators as policy tools and improve their potential to be incorporated into future sets of indicators to track progress towards global biodiversity targets