DSpace Repository :: Browsing by Author "Linsen, Lars"

Browsing by Author "Linsen, Lars"

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Axes Bundling and Brushing in Star Coordinates
(The Eurographics Association, 2021) Rave, Hennes; Molchanov, Vladimir; Linsen, Lars; Andres, Bjoern and Campen, Marcel and Sedlmair, Michael
Visual analysis of multidimensional data commonly involves dimensionality reduction to project the data samples into a lowerdimensional visual space. Star coordinates (SC) provide a means to explore the multidimensional data distribution by interactively changing the linear projection matrix. While SC have the advantages of being intuitive, allowing for relating the data samples to their original dimensions, having low computation costs, and scaling well with the number of data samples, they have the disadvantages of not scaling well to larger number of dimensions and being restricted to linear projections. We address these short-comings by introducing novel SC interactions. First, interactive bundling of axes is proposed to reduce the number of dimensions. While bundles are fully customizable, the bundling interactions are supported by visualizations of correlation matrices and hierarchical axes clustering dendrograms. Second, we enhance classical region brushing in SC projections with axes brushing, which allows for multidimensional cluster selection, even if two (separable) clusters are projected to the same area of the visible space. Axes brushing is supported by visualizing 1D histograms of data distributions along the SC axes. Our brushing interactions alleviate the restriction of SC to linear projections. The integration of histograms into SC also eases other interactions such as moving axes to change the projection matrix. A user study evaluates how analysis tasks for labeled and unlabeled multidimensional data can benefit from our extensions.
Evaluating Data‐type Heterogeneity in Interactive Visual Analyses with Parallel Axes
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2022) Matute, José; Linsen, Lars; Hauser, Helwig and Alliez, Pierre
The application of parallel axes for the interactive visual analysis of multidimensional data is a widely used concept. While multidimensional data sets are commonly heterogeneous in nature, i.e. data items contain both numerical and categorical (including ordinal) attribute values, the use of parallel axes often assumes either numerical or categorical attributes. While Parallel Coordinates and their large variety of extensions focus on numerical data, Parallel Sets and related methods focus on categorical attributes. While both concepts allow for displaying heterogeneous data, no clear strategies have been defined for representing categories in Parallel Coordinates or discretization of continuous ranges in Parallel Sets. In practice, type conversion as a pre‐processing step can be used as well as coordinated views of numerical and categorical data visualizations. We evaluate traditional and state‐of‐the‐art approaches with respect to the interplay of categorical and numerical dimensions for querying probability‐based events. We also compare against a heterogeneous Parallel Coordinates/Parallel Set approach with a novel interface between categorical and numerical axes . We show that approaches for mapping categorical data to numerical axis representations can lead to lower accuracy in answering probability‐based questions and higher response times than hybrid approaches in multiple‐event scenarios.
Explorative Visual Analysis of Spatio-temporal Regions to Detect Hemodynamic Biomarker Candidates
(The Eurographics Association, 2022) Derstroff, Adrian; Leistikow, Simon; Nahardani, Ali; Ebrahimi, Mahyasadat; Hoerr, Verena; Linsen, Lars; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Biomarkers are measurable biological properties that allow for distinguishing subjects of different cohorts such as healthy vs. diseased. In the context of diagnosing diseases of the cardiovascular system, researchers aim - among others - at detecting biomarkers in the form of spatio-temporal regions of blood flow obtained by medical imaging or of derived hemodynamical parameters. As the search space for such biomarkers in time-varying volumetric multi-field data is extremely large, we present an interactive visual exploration system to support the analysis of the potential of spatio-temporal regions to discriminate cohorts.
Interactive Generation of 1D Embeddings from 2D Multi-dimensional Data Projections
(The Eurographics Association, 2020) Ngo, Quynh Quang; Linsen, Lars; Krüger, Jens and Niessner, Matthias and Stückler, Jörg
Visual analysis of multi-dimensional data is commonly supported by mapping the data to a 2D embedding. When analyzing a sequence of multi-dimensional data, e.g., in case of temporal data, the usage of 1D embeddings allows for plotting the entire sequence in a 2D layout. Despite the good performance in generating 2D embeddings, 1D embeddings often exhibit a much lower quality for pattern recognition tasks. We propose to overcome the issue by involving the user to generate 1D embeddings of multi-dimensional data in a two-step procedure: We first generate a 2D embedding and then leave the task of reducing the 2D to a 1D embedding to the user. We demonstrate that an interactive generation of 1D embeddings from 2D projected views can be performed efficiently, effectively, and targeted towards an analysis task. We compare the performance of our approach against automatically generated 1D and 2D embeddings involving a user study for our interactive approach. We test the 1D approaches when being applied to time-varying multi-dimensional data.
Interactive Visual Analysis of Regional Time Series Correlation in Multi-field Climate Ensembles
(The Eurographics Association, 2023) Evers, Marina; Böttinger, Michael; Linsen, Lars; Dutta, Soumya; Feige, Kathrin; Rink, Karsten; Zeckzer, Dirk
Spatio-temporal multi-field data resulting from ensemble simulations are commonly used in climate research to investigate possible climatic developments and their certainty. One analysis goal is the investigation of possible correlations among different spatial regions in the different fields to find regions of related behavior. We propose an interactive visual analysis approach that focuses on the analysis of correlations in spatio-temporal ensemble data. Our approach allows for finding correlations between spatial regions in different fields. Detection of clusters of strongly correlated spatial regions is supported by lower-dimensional embeddings. Then, groups can be selected and investigated in detail, e.g., to study the temporal evolution of the selected group, their Fourier spectra or the distribution of the correlations over the different ensemble members. We apply our approach to selected 2D scalar fields of a large ensemble climate simulation and demonstrate the utility of our tool with several use cases.
Interactive Visual Similarity Analysis of Measured and Simulated Multi-field Tubular Flow Ensembles
(The Eurographics Association, 2020) Leistikow, Simon; Nahardani, Ali; Hoerr, Verena; Linsen, Lars; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
Tubular flow analysis plays an important role in many fields, such as for blood flow analysis in medicine, e.g., for the diagnosis of cardiovascular diseases and treatment planning. Phase-contrast magnetic resonance imaging (PC-MRI) allows for noninvasive in vivo-measurements of such tubular flow, but may suffer from imaging artifacts. New acquisition techniques (or sequences) that are being developed to increase image quality and reduce measurement time have to be validated against the current clinical standard. Computational Fluid Dynamics (CFD), on the other hand, allows for simulating noise-free tubular flow, but optimization of the underlying model depends on multiple parameters and can be a tedious procedure that may run into local optima. Data assimilation is the process of optimally combining the data from both PC-MRI and CFD domains. We present an interactive visual analysis approach to support domain experts in the above-mentioned fields by addressing PC-MRI and CFD ensembles as well as their combination. We develop a multi-field similarity measure including both scalar and vector fields to explore common hemodynamic parameters, and visualize the evolution of the ensemble similarities in a low-dimensional embedding. Linked views to spatial visualizations of selected time steps support an in-detail analysis of the spatio-temporal distribution of differences. To evaluate our system, we reached out to experts from the PC-MRI and CFD domains and summarize their feedback.
SimilarityNet: A Deep Neural Network for Similarity Analysis Within Spatio-temporal Ensembles
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Huesmann, Karim; Linsen, Lars; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Latent feature spaces of deep neural networks are frequently used to effectively capture semantic characteristics of a given dataset. In the context of spatio-temporal ensemble data, the latent space represents a similarity space without the need of an explicit definition of a field similarity measure. Commonly, these networks are trained for specific data within a targeted application. We instead propose a general training strategy in conjunction with a deep neural network architecture, which is readily applicable to any spatio-temporal ensemble data without re-training. The latent-space visualization allows for a comprehensive visual analysis of patterns and temporal evolution within the ensemble. With the use of SimilarityNet, we are able to perform similarity analyses on large-scale spatio-temporal ensembles in less than a second on commodity consumer hardware. We qualitatively compare our results to visualizations with established field similarity measures to document the interpretability of our latent space visualizations and show that they are feasible for an in-depth basic understanding of the underlying temporal evolution of a given ensemble.
Studying the Effect of Tissue Properties on Radiofrequency Ablation by Visual Simulation Ensemble Analysis
(The Eurographics Association, 2022) Heimes, Karl; Evers, Marina; Gerrits, Tim; Gyawali, Sandeep; Sinden, David; Preusser, Tobias; Linsen, Lars; Renata G. Raidou; Björn Sommer; Torsten W. Kuhlen; Michael Krone; Thomas Schultz; Hsiang-Yun Wu
Radiofrequency ablation is a minimally invasive, needle-based medical treatment to ablate tumors by heating due to absorption of radiofrequency electromagnetic waves. To ensure the complete target volume is destroyed, radiofrequency ablation simulations are required for treatment planning. However, the choice of tissue properties used as parameters during simulation induce a high uncertainty, as the tissue properties are strongly patient-dependent. To capture this uncertainty, a simulation ensemble can be created. Understanding the dependency of the simulation outcome on the input parameters helps to create improved simulation ensembles by focusing on the main sources of uncertainty and, thus, reducing computation costs. We present an interactive visual analysis tool for radiofrequency ablation simulation ensembles to target this objective. Spatial 2D and 3D visualizations allow for the comparison of ablation results of individual simulation runs and for the quantification of differences. Simulation runs can be interactively selected based on a parallel coordinates visualization of the parameter space. A 3D parameter space visualization allows for the analysis of the ablation outcome when altering a selected tissue property for the three tissue types involved in the ablation process. We discuss our approach with domain experts working on the development of new simulation models and demonstrate the usefulness of our approach for analyzing the influence of different tissue properties on radiofrequency ablations.
TrustVis 2019: Frontmatter
(The Eurographics Association, 2019) Kosara, Robert; Lawonn, Kai; Linsen, Lars; Smit, Noeska; Kosara, Robert and Lawonn, Kai and Linsen, Lars and Smit, Noeska
Uncertainty-aware Visualization of Regional Time Series Correlation in Spatio-temporal Ensembles
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Evers, Marina; Huesmann, Karim; Linsen, Lars; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Given a time-varying scalar field, the analysis of correlations between different spatial regions, i.e., the linear dependence of time series within these regions, provides insights into the structural properties of the data. In this context, regions are connected components of the spatial domain with high time series correlations. The detection and analysis of such regions is often performed globally, which requires pairwise correlation computations that are quadratic in the number of spatial data samples. Thus, operations based on all pairwise correlations are computationally demanding, especially when dealing with ensembles that model the uncertainty in the spatio-temporal phenomena using multiple simulation runs. We propose a two-step procedure: In a first step, we map the spatial samples to a 3D embedding based on a pairwise correlation matrix computed from the ensemble of time series. The 3D embedding allows for a one-to-one mapping to a 3D color space such that the outcome can be visually investigated by rendering the colors for all samples in the spatial domain. In a second step, we generate a hierarchical image segmentation based on the color images. From then on, we can visually analyze correlations of regions at all levels in the hierarchy within an interactive setting, which includes the uncertainty-aware analysis of the region's time series correlation and respective time lags.
Uni- and Multi-modal Uncertainty Visualization in 2D Scalar Field Ensembles
(The Eurographics Association, 2019) Gebauer, Eike; Linsen, Lars; Kosara, Robert and Lawonn, Kai and Linsen, Lars and Smit, Noeska
The aim of uncertainty-aware scalar field visualization is to convey the most likely case, but also the uncertainty associated with it. In scientific simulations, uncertainty can be modeled using an ensemble approach. Statistics are then drawn from the ensemble outcome to compute the most likely case and its uncertainty. However, the statistical distributions do not necessarily need to be uni-modal. We present an approach to visualize uncertain 2D scalar fields that extends existing uni-modal distributions based on colored heightfields and 2D glyphs to multi-modal ones. We compare the approaches by conducting user experiments for both the uni- and multi-modal case.
Visual Analysis of Regional Anomalies in Myocardial Motion
(The Eurographics Association, 2018) Sheharyar, Ali; Ruh, Alexander; Aristova, Maria; Scott, Michael; Jarvis, Kelly; Elbaz, Mohammed; Dolan, Ryan; Schnell, Susanne; Lin, Kal; Carr, James; Markl, Michael; Bouhali, Othmane; Linsen, Lars; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
Regional anomalies in the myocardial motion of the left ventricle (LV) are important biomarkers for several cardiac diseases. Myocardial motion can be captured using a velocity-encoded magnetic resonance imaging method called tissue phase mapping (TPM). The acquired data are pre-processed and represented as regional velocities in cylindrical coordinates at three short-axis slices of the left ventricle over one cardiac cycle. We use a spatio-temporal visualization based on a radial layout where the myocardial regions are laid out in an angular pattern similar to the American Heart Association (AHA) model and the temporal dimension increases with increasing radius. To detect anomalies, we compare patient data against the myocardial motion of a cohort of healthy volunteers. For the healthy volunteer cohort, we compute nested envelopes of central regions for the time series of each region and each of the three velocity directions based on the concept of functional boxplots. A quantitative depiction of deviations from the spatio-temporal pattern of healthy heart motion allows for quick detection of regions of interests, which can then be analyzed in more detail by looking at the actual time series. We evaluated our approach in a qualitative user study with imaging and medical experts. The participants appreciated the proposed encoding and considered it a substantial improvement over the current methods.
Visual Analytics of Simulation Ensembles for Network Dynamics
(The Eurographics Association, 2019) Ngo, Quynh Quang; Hütt, Marc-Thorsten; Linsen, Lars; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
A central question in the field of Network Science is to analyze the role of a given network topology on the dynamical behavior captured by time-varying simulations executed on the network. These dynamical systems are also influenced by global simulation parameters. We present a visual analytics approach that supports the investigation of the impact of the parameter settings, i.e., how parameter choices change the role of network topology on the simulations' dynamics. To answer this question, we are analyzing ensembles of simulation runs with different parameter settings executed on a given network topology. We relate the nodes' topological structures to their dynamical similarity in a 2D plot based on an interactively defined hierarchy of topological properties and a 1D embedding for the dynamical similarity. We evaluate interactively defined topological groups with respect to matching dynamical behavior, which we visually encode as graphs of the function of the considered simulation parameter. Interactive filtering and coordinated views allow for a detailed analysis of the parameter space with respect to topology-dynamics relations. Our visual analytics approach is applied to scenarios for excitable dynamics on synthetic and real brain connectome networks.
A Visual Analytics Tool for Cohorts in Motion Data
(The Eurographics Association, 2019) Sheharyar, Ali; Ruh, Alexander; Valkov, Dimitar; Markl, Michael; Bouhali, Othmane; Linsen, Lars; Schulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michael
Motion data are curves over time in a 1D, 2D, or 3D space. To analyze sets of curves, machine learning methods can be applied to cluster them and detect outliers. However, often metadata or prior knowledge of the analyst drives the analysis by defining cohorts. Our goal is to provide a flexible system for comparative visual analytics of cohorts in motion data. The analyst interactively defines cohorts by filtering on metadata properties. We, then, apply machine learning and statistical methods to extract the main features of each cohort. Summarizations of these features are visually encoded using, in particular, boxplots and their extensions to functional and curve boxplots, depending on the number of selected dimensions of the space. These summarizations allow for an intuitive comparative visual analysis of cohorts in a juxtaposed or superimposed representation. Our system provides full flexibility in defining cohorts, selecting time intervals and spatial dimensions, and adjusting the aggregation level of summarizations. Comparison of an individual sample against a cohort is also supported. We demonstrate the functionality, effectiveness, and flexibility of our system by applying it to a range of diverse motion data sets.
Visual Ensemble Analysis to Study the Influence of Hyper-parameters on Training Deep Neural Networks
(The Eurographics Association, 2019) Hamid, Sagad; Derstroff, Adrian; Klemm, Sören; Ngo, Quynh Quang; Jiang, Xiaoyi; Linsen, Lars; Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko
A good deep neural network design allows for efficient training and high accuracy. The training step requires a suitable choice of several hyper-parameters. Limited knowledge exists on how the hyper-parameters impact the training process, what is the interplay of multiple hyper-parameters, and what is the interrelation of hyper-parameters and network topology. In this paper, we present a structured analysis towards these goals by investigating an ensemble of training runs.We propose a visual ensemble analysis based on hyper-parameter space visualizations, performance visualizations, and visualizing correlations of topological structures. As a proof of concept, we apply our approach to deep convolutional neural networks.

Browsing by Author "Linsen, Lars"

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