22 research outputs found
TimeLighting: Guidance-enhanced Exploration of 2D Projections of Temporal Graphs
In temporal (or event-based) networks, time is a continuous axis, with
real-valued time coordinates for each node and edge. Computing a layout for
such graphs means embedding the node trajectories and edge surfaces over time
in a 2D + t space, known as the space-time cube. Currently, these space-time
cube layouts are visualized through animation or by slicing the cube at regular
intervals. However, both techniques present problems ranging from sub-par
performance on some tasks to loss of precision. In this paper, we present
TimeLighting, a novel visual analytics approach to visualize and explore
temporal graphs embedded in the space-time cube. Our interactive approach
highlights the node trajectories and their mobility over time, visualizes node
"aging", and provides guidance to support users during exploration. We evaluate
our approach through two case studies, showing the system's efficacy in
identifying temporal patterns and the role of the guidance features in the
exploration process.Comment: Appears in the Proceedings of the 31st International Symposium on
Graph Drawing and Network Visualization (GD 2023
Slice and Dice: A Physicalization Workflow for Anatomical Edutainment
During the last decades, anatomy has become an interesting topic in
education---even for laymen or schoolchildren. As medical imaging techniques
become increasingly sophisticated, virtual anatomical education applications
have emerged. Still, anatomical models are often preferred, as they facilitate
3D localization of anatomical structures. Recently, data physicalizations
(i.e., physical visualizations) have proven to be effective and
engaging---sometimes, even more than their virtual counterparts. So far,
medical data physicalizations involve mainly 3D printing, which is still
expensive and cumbersome. We investigate alternative forms of physicalizations,
which use readily available technologies (home printers) and inexpensive
materials (paper or semi-transparent films) to generate crafts for anatomical
edutainment. To the best of our knowledge, this is the first computer-generated
crafting approach within an anatomical edutainment context. Our approach
follows a cost-effective, simple, and easy-to-employ workflow, resulting in
assemblable data sculptures (i.e., semi-transparent sliceforms). It primarily
supports volumetric data (such as CT or MRI), but mesh data can also be
imported. An octree slices the imported volume and an optimization step
simplifies the slice configuration, proposing the optimal order for easy
assembly. A packing algorithm places the resulting slices with their labels,
annotations, and assembly instructions on a paper or transparent film of
user-selected size, to be printed, assembled into a sliceform, and explored. We
conducted two user studies to assess our approach, demonstrating that it is an
initial positive step towards the successful creation of interactive and
engaging anatomical physicalizations
Finding Nano-\"Otzi: Semi-Supervised Volume Visualization for Cryo-Electron Tomography
Cryo-Electron Tomography (cryo-ET) is a new 3D imaging technique with
unprecedented potential for resolving submicron structural detail. Existing
volume visualization methods, however, cannot cope with its very low
signal-to-noise ratio. In order to design more powerful transfer functions, we
propose to leverage soft segmentation as an explicit component of visualization
for noisy volumes. Our technical realization is based on semi-supervised
learning where we combine the advantages of two segmentation algorithms. A
first weak segmentation algorithm provides good results for propagating sparse
user provided labels to other voxels in the same volume. This weak segmentation
algorithm is used to generate dense pseudo labels. A second powerful
deep-learning based segmentation algorithm can learn from these pseudo labels
to generalize the segmentation to other unseen volumes, a task that the weak
segmentation algorithm fails at completely. The proposed volume visualization
uses the deep-learning based segmentation as a component for segmentation-aware
transfer function design. Appropriate ramp parameters can be suggested
automatically through histogram analysis. Finally, our visualization uses
gradient-free ambient occlusion shading to further suppress visual presence of
noise, and to give structural detail desired prominence. The cryo-ET data
studied throughout our technical experiments is based on the highest-quality
tilted series of intact SARS-CoV-2 virions. Our technique shows the high impact
in target sciences for visual data analysis of very noisy volumes that cannot
be visualized with existing techniques
Cardiac biophysical detailed synergetic modality rendering and visible correlation
The heart is a vital organ in the human body. Research and treatment for the heart have made remarkable progress, and the functional mechanisms of the heart have been simulated and rendered through the construction of relevant models. The current methods for rendering cardiac functional mechanisms only consider one type of modality, which means they cannot show how different types of modality, such as physical and physiological, work together. To realistically represent the three-dimensional synergetic biological modality of the heart, this paper proposes a WebGL-based cardiac synergetic modality rendering framework to visualize the cardiac physical volume data and present synergetic correspondence rendering of the cardiac electrophysiological modality. By constructing the biological detailed interactive histogram, users can implement local details rendering for the heart, which could reveal the cardiac biology details more clearly. We also present cardiac physical-physiological correlation visualization to explore cardiac biological association characteristics. Experimental results show that the proposed framework can provide favorable cardiac biological detailed synergetic modality rendering results in terms of both effectiveness and efficiency. Compared with existing methods, the framework can facilitate the study of the internal mechanism of the heart and subsequently deduce the process of initiation, development, and transformation from a healthy heart to an ill one, and thereby improve the diagnosis and treatment of cardiac disorders