Inviwo - A Visualization System with Usage Abstraction Levels

A Visual Environment for Data Driven Protein Modeling and Validation

In structural biology, validation and verification of new atomic models are crucial and necessary steps which limit the production of reliable molecular models for publications and databases. An atomic model is the result of meticulous modeling and matching and is evaluated using a variety of metrics that provide clues to improve and refine the model so it fits our understanding of molecules and physical constraints. In cryo electron microscopy (cryo-EM) the validation is also part of an iterative modeling process in which there is a need to judge the quality of the model during the creation phase. A shortcoming is that the process and results of the validation are rarely communicated using visual metaphors. This work presents a visual framework for molecular validation. The framework was developed in close collaboration with domain experts in a participatory design process. Its core is a novel visual representation based on 2D heatmaps that shows all available validation metrics in a linear fashion, presenting a global overview of the atomic model and provide domain experts with interactive analysis tools. Additional information stemming from the underlying data, such as a variety of local quality measures, is used to guide the user's attention toward regions of higher relevance. Linked with the heatmap is a three-dimensional molecular visualization providing the spatial context of the structures and chosen metrics. Additional views of statistical properties of the structure are included in the visual framework. We demonstrate the utility of the framework and its visual guidance with examples from cryo-EM.

u-track3D: Measuring, navigating, and validating dense particle trajectories in three dimensions

We describe u-track3D, a software package that extends the versatile u-track framework established in 2D to address the specific challenges of 3D particle tracking. First, we present the performance of the new package in quantifying a variety of intracellular dynamics imaged by multiple 3D microcopy platforms and on the standard 3D test dataset of the particle tracking challenge. These analyses indicate that u-track3D presents a tracking solution that is competitive to both conventional and deep-learning-based approaches. We then present the concept of dynamic region of interest (dynROI), which allows an experimenter to interact with dynamic 3D processes in 2D views amenable to visual inspection. Third, we present an estimator of trackability that automatically defines a score for every trajectory, thereby overcoming the challenges of trajectory validation by visual inspection. With these combined strategies, u-track3D provides a complete framework for unbiased studies of molecular processes in complex volumetric sequences.

VIAMD: a Software for Visual Interactive Analysis of Molecular Dynamics

The typical workflow in molecular dynamics (MD) analysis requires several separate tools, often resulting in a lack of synergy and interaction between the individual analysis steps. This article presents VIAMD, an application designed to address this issue by integrating a 3D visualization of molecular trajectories with flexible analysis components. VIAMD uses an interactive scripting interface, allowing for property definition and evaluation. The application provides context-aware suggestions and expression feedback through information and visualizations. The user-defined properties can be explored and analyzed through the various components. This enables correlation with spatial conformations, statistical analysis of distributions, and powerful aggregation of multidimensional properties such as spatial distribution functions. VIAMD has the potential to advance research in many scientific disciplines and is a promising solution for improving the workflow of MD visualization and analysis.

Completing the view - histologic insights from circular AAA specimen including 3D imaging : A methodologic approach towards histologic analysis of circumferential AAA samples

Abdominal aortic aneurysm (AAA) is a pathologic enlargement of the infrarenal aorta with an associated risk of rupture. However, the responsible mechanisms are only partially understood. Based on murine and human samples, a heterogeneous distribution of characteristic pathologic features across the aneurysm circumference is expected. Yet, complete histologic workup of the aneurysm sac is scarcely reported. Here, samples from five AAAs covering the complete circumference partially as aortic rings are investigated by histologic means (HE, EvG, immunohistochemistry) and a new method embedding the complete ring. Additionally, two different methods of serial histologic section alignment are applied to create a 3D view. The typical histopathologic features of AAA, elastic fiber degradation, matrix remodeling with collagen deposition, calcification, inflammatory cell infiltration and thrombus coverage were distributed without recognizable pattern across the aneurysm sac in all five patients. Analysis of digitally scanned entire aortic rings facilitates the visualization of these observations. Immunohistochemistry is feasible in such specimen, however, tricky due to tissue disintegration. 3D image stacks were created using open-source and non-generic software correcting for non-rigid warping between consecutive sections. Secondly, 3D image viewers allowed visualization of in-depth changes of the investigated pathologic hallmarks. In conclusion, this exploratory descriptive study demonstrates a heterogeneous histomorphology around the AAA circumference. Warranting an increased sample size, these results might need to be considered in future mechanistic research, especially in reference to intraluminal thrombus coverage. 3D histology of such circular specimen could be a valuable visualization tool for further analysis.

Can Image Data Facilitate Reproducibility of Graphics and Visualizations? Toward a Trusted Scientific Practice

Reproducibility is a cornerstone of good scientific practice; however, the ongoing "reproducibility crisis" shows that we still need to improve the way we are doing research currently. Reproducibility is crucial because it enables both the comparison to existing techniques as well as the composition and improvement of existing approaches. It can also increase trust in the respective results, which is paramount for adoption in further research and applications. While there are already many initiatives and approaches with different complexity aimed at enabling reproducible research in the context of visualization, we argue for an alternative, lightweight approach that documents the most relevant parameters with minimal overhead. It still complements complex approaches well, and integration with any existing tool or system is simple. Our approach uses the images produced by visualizations and seamlessly piggy-backs everyday communication and research collaborations, publication authoring, public outreach, and internal note-taking. We exemplify how our approach supports day-to-day work and discuss limitations and how they can be countered.

TopoCluster: A Localized Data Structure for Topology-Based Visualization

Unstructured data are collections of points with irregular topology, often represented through simplicial meshes, such as triangle and tetrahedral meshes. Whenever possible such representations are avoided in visualization since they are computationally demanding if compared with regular grids. In this work, we aim at simplifying the encoding and processing of simplicial meshes. The article proposes TopoCluster, a new localized data structure for tetrahedral meshes. TopoCluster provides efficient computation of the connectivity of the mesh elements with a low memory footprint. The key idea of TopoCluster is to subdivide the simplicial mesh into clusters. Then, the connectivity information is computed locally for each cluster and discarded when it is no longer needed. We define two instances of TopoCluster. The first instance prioritizes time efficiency and provides only a modest savings in memory, while the second instance drastically reduces memory consumption up to an order of magnitude with respect to comparable data structures. Thanks to the simple interface provided by TopoCluster, we have been able to integrate both data structures into the existing Topological Toolkit (TTK) framework. As a result, users can run any plugin of TTK using TopoCluster without changing a single line of code.

Direct Transmittance Estimation in Heterogeneous Participating Media Using Approximated Taylor Expansions

Evaluating the transmittance between two points along a ray is a key component in solving the light transport through heterogeneous participating media and entails computing an intractable exponential of the integrated medium's extinction coefficient. While algorithms for estimating this transmittance exist, there is a lack of theoretical knowledge about their behaviour, which also prevent new theoretically sound algorithms from being developed. For this purpose, we introduce a new class of unbiased transmittance estimators based on random sampling or truncation of a Taylor expansion of the exponential function. In contrast to classical tracking algorithms, these estimators are non-analogous to the physical light transport process and directly sample the underlying extinction function without performing incremental advancement. We present several versions of the new class of estimators, based on either importance sampling or Russian roulette to provide finite unbiased estimators of the infinite Taylor series expansion. We also show that the well known ratio tracking algorithm can be seen as a special case of the new class of estimators. Lastly, we conduct performance evaluations on both the central processing unit (CPU) and the graphics processing unit (GPU), and the results demonstrate that the new algorithms outperform traditional algorithms for heterogeneous mediums.

Level of Detail Exploration of Electronic Transition Ensembles using Hierarchical Clustering

We present a pipeline for the interactive visual analysis and exploration of molecular electronic transition ensembles. Each ensemble member is specified by a molecular configuration, the charge transfer between two molecular states, and a set of physical properties. The pipeline is targeted towards theoretical chemists, supporting them in comparing and characterizing electronic transitions by combining automatic and interactive visual analysis. A quantitative feature vector characterizing the electron charge transfer serves as the basis for hierarchical clustering as well as for the visual representations. The interface for the visual exploration consists of four components. A dendrogram provides an overview of the ensemble. It is augmented with a level of detail glyph for each cluster. A scatterplot using dimensionality reduction provides a second visualization, highlighting ensemble outliers. Parallel coordinates show the correlation with physical parameters. A spatial representation of selected ensemble members supports an in‐depth inspection of transitions in a form that is familiar to chemists. All views are linked and can be used to filter and select ensemble members. The usefulness of the pipeline is shown in three different case studies.

A Review of Three-Dimensional Medical Image Visualization

Importance. Medical images are essential for modern medicine and an important research subject in visualization. However, medical experts are often not aware of the many advanced three-dimensional (3D) medical image visualization techniques that could increase their capabilities in data analysis and assist the decision-making process for specific medical problems. Our paper provides a review of 3D visualization techniques for medical images, intending to bridge the gap between medical experts and visualization researchers.Highlights. Fundamental visualization techniques are revisited for various medical imaging modalities, from computational tomography to diffusion tensor imaging, featuring techniques that enhance spatial perception, which is critical for medical practices. The state-of-the-art of medical visualization is reviewed based on a procedure-oriented classification of medical problems for studies of individuals and populations. This paper summarizes free software tools for different modalities of medical images designed for various purposes, including visualization, analysis, and segmentation, and it provides respective Internet links.Conclusions. Visualization techniques are a useful tool for medical experts to tackle specific medical problems in their daily work. Our review provides a quick reference to such techniques given the medical problem and modalities of associated medical images. We summarize fundamental techniques and readily available visualization tools to help medical experts to better understand and utilize medical imaging data. This paper could contribute to the joint effort of the medical and visualization communities to advance precision medicine.

LEVERSC: Cross-Platform Scriptable Multichannel 3-D Visualization for Fluorescence Microscopy Images

We describe a new open-source program called LEVERSC to address the challenges of visualizing the multi-channel 3-D images prevalent in biological microscopy. LEVERSC uses a custom WebGL hardware-accelerated raycasting engine unique in its combination of rendering quality and performance, particularly for multi-channel data. Key features include platform independence, quantitative visualization through interactive voxel localization, and reproducible dynamic visualization via the scripting interface. LEVERSC is fully scriptable and interactive, and works with MATLAB, Python and Java/ImageJ.

Deep Volumetric Ambient Occlusion

We present a novel deep learning based technique for volumetric ambient occlusion in the context of direct volume rendering. Our proposed Deep Volumetric Ambient Occlusion (DVAO) approach can predict per-voxel ambient occlusion in volumetric data sets, while considering global information provided through the transfer function. The proposed neural network only needs to be executed upon change of this global information, and thus supports real-time volume interaction. Accordingly, we demonstrate DVAO's ability to predict volumetric ambient occlusion, such that it can be applied interactively within direct volume rendering. To achieve the best possible results, we propose and analyze a variety of transfer function representations and injection strategies for deep neural networks. Based on the obtained results we also give recommendations applicable in similar volume learning scenarios. Lastly, we show that DVAO generalizes to a variety of modalities, despite being trained on computed tomography data only.