Task-based Augmented Contour Trees with Fibonacci Heaps

This paper presents a new algorithm for the fast, shared memory, multi-core computation of augmented contour trees on triangulations. In contrast to most existing parallel algorithms our technique computes augmented trees, enabling the full extent of contour tree based applications including data segmentation. Our approach completely revisits the traditional, sequential contour tree algorithm to re-formulate all the steps of the computation as a set of independent local tasks. This includes a new computation procedure based on Fibonacci heaps for the join and split trees, two intermediate data structures used to compute the contour tree, whose constructions are efficiently carried out concurrently thanks to the dynamic scheduling of task parallelism. We also introduce a new parallel algorithm for the combination of these two trees into the output global contour tree. Overall, this results in superior time performance in practice, both in sequential and in parallel thanks to the OpenMP task runtime. We report performance numbers that compare our approach to reference sequential and multi-threaded implementations for the computation of augmented merge and contour trees. These experiments demonstrate the run-time efficiency of our approach and its scalability on common workstations. We demonstrate the utility of our approach in data segmentation applications

Incorporating 3-dimensional models in online articles

Introduction The aims of this article are to introduce the capability to view and interact with 3-dimensional (3D) surface models in online publications, and to describe how to prepare surface models for such online 3D visualizations. Methods Three-dimensional image analysis methods include image acquisition, construction of surface models, registration in a common coordinate system, visualization of overlays, and quantification of changes. Cone-beam computed tomography scans were acquired as volumetric images that can be visualized as 3D projected images or used to construct polygonal meshes or surfaces of specific anatomic structures of interest. The anatomic structures of interest in the scans can be labeled with color (3D volumetric label maps), and then the scans are registered in a common coordinate system using a target region as the reference. The registered 3D volumetric label maps can be saved in.obj,.ply,.stl, or.vtk file formats and used for overlays, quantification of differences in each of the 3 planes of space, or color-coded graphic displays of 3D surface distances. Results All registered 3D surface models in this study were saved in.vtk file format and loaded in the Elsevier 3D viewer. In this study, we describe possible ways to visualize the surface models constructed from cone-beam computed tomography images using 2D and 3D figures. The 3D surface models are available in the article's online version for viewing and downloading using the reader's software of choice. These 3D graphic displays are represented in the print version as 2D snapshots. Overlays and color-coded distance maps can be displayed using the reader's software of choice, allowing graphic assessment of the location and direction of changes or morphologic differences relative to the structure of reference. The interpretation of 3D overlays and quantitative color-coded maps requires basic knowledge of 3D image analysis. Conclusions When submitting manuscripts, authors can now upload 3D models that will allow readers to interact with or download them. Such interaction with 3D models in online articles now will give readers and authors better understanding and visualization of the results

Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks

Retinopathy of Prematurity (ROP) is an ocular disease observed in premature babies, considered one of the largest preventable causes of childhood blindness. Problematically, the visual indicators of ROP are not well understood and neonatal fundus images are usually of poor quality and resolution. We investigate two ways to aid clinicians in ROP detection using convolutional neural networks (CNN): (1) We fine-tune a pretrained GoogLeNet as a ROP detector and with small modifications also return an approximate Bayesian posterior over disease presence. To the best of our knowledge, this is the first completely automated ROP detection system. (2) To further aid grading, we train a second CNN to return novel feature map visualizations of pathologies, learned directly from the data. These feature maps highlight discriminative information, which we believe may be used by clinicians with our classifier to aid in screening

Fractional anisotropy distributions in 2- to 6-year-old children with autism

Increasing evidence suggests that autism is a disorder of distributed neural networks that may exhibit abnormal developmental trajectories. Characterisation of white matter early in the developmental course of the disorder is critical to understanding these aberrant trajectories

A Semantic Web SKOS Vocabulary Service for Open Knowledge Organization Systems

In this article, the Basel Register of Thesauri, Ontologies & Classications (BARTOC.org) is introduced to raise awareness for an integrated, full terminology registry for knowledge organization systems. Recently, researchers have shown an increased interest in such a single access point for controlled vocabularies. The paper outlines BARTOC's technical implementation, system architecture, and services in the light of semantic technologies. Its central thesis is that if the KOS community agreed on BARTOC as one of their main terminology registries, all involved parties would benefit from linked open knowledge organization systems