VCBM 2020: Eurographics Workshop on Visual Computing for Biology and Medicine
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Browsing VCBM 2020: Eurographics Workshop on Visual Computing for Biology and Medicine by Subject "Applied computing"
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Item Analyzing Protein Similarity by Clustering Molecular Surface Maps(The Eurographics Association, 2020) Schatz, Karsten; Frieß, Florian; Schäfer, Marco; Ertl, Thomas; Krone, Michael; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata GeorgiaMany biochemical and biomedical applications like protein engineering or drug design are concerned with finding functionally similar proteins, however, this remains to be a challenging task. We present a new imaged-based approach for identifying and visually comparing proteins with similar function that builds on the hierarchical clustering of Molecular Surface Maps. Such maps are two-dimensional representations of complex molecular surfaces and can be used to visualize the topology and different physico-chemical properties of proteins. Our method is based on the idea that visually similar maps also imply a similarity in the function of the mapped proteins. To determine map similarity we compute descriptive feature vectors using image moments, color moments, or a Convolutional Neural Network and use them for a hierarchical clustering of the maps. We show that image similarity as found by our clustering corresponds to functional similarity of mapped proteins by comparing our results to the BRENDA database, which provides a hierarchical function-based annotation of enzymes. We also compare our results to the TM-score, which is a similarity value for pairs of arbitrary proteins. Our visualization prototype supports the entire workflow from map generation, similarity computing to clustering and can be used to interactively explore and analyze the results.Item Feature Exploration using Local Frequency Distributions in Computed Tomography Data(The Eurographics Association, 2020) Falk, Martin; Ljung, Patric; Lundström, Claes; Ynnerman, Anders; Hotz, Ingrid; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata GeorgiaFrequency distributions (FD) are an important instrument when analyzing and investigating scientific data. In volumetric visualization, for example, frequency distributions visualized as histograms, often assist the user in the process of designing transfer function (TF) primitives. Yet a single point in the distribution can correspond to multiple features in the data, particularly in low-dimensional TFs that dominate time-critical domains such as health care. In this paper, we propose contributions to the area of medical volume data exploration, in particular Computed Tomography (CT) data, based on the decomposition of local frequency distributions (LFD). By considering the local neighborhood utilizing LFDs we can incorporate a measure for neighborhood similarity to differentiate features thereby enhancing the classification abilities of existing methods. This also allows us to link the attribute space of the histogram with the spatial properties of the data to improve the user experience and simplify the exploration step. We propose three approaches for data exploration which we illustrate with several visualization cases highlighting distinct features that are not identifiable when considering only the global frequency distribution. We demonstrate the power of the method on selected datasets.Item InShaDe: Invariant Shape Descriptors for Visual Analysis of Histology 2D Cellular and Nuclear Shapes(The Eurographics Association, 2020) Agus, Marco; Al-Thelaya, Khaled; Cali, Corrado; Boido, Marina M.; Yang, Yin; Pintore, Giovanni; Gobbetti, Enrico; Schneider, Jens; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata GeorgiaWe present a shape processing framework for visual exploration of cellular nuclear envelopes extracted from histology images. The framework is based on a novel shape descriptor of closed contours relying on a geodesically uniform resampling of discrete curves to allow for discrete differential-geometry-based computation of unsigned curvature at vertices and edges. Our descriptor is, by design, invariant under translation, rotation and parameterization. Moreover, it additionally offers the option for uniform-scale-invariance. The optional scale-invariance is achieved by scaling features to z-scores, while invariance under parameterization shifts is achieved by using elliptic Fourier analysis (EFA) on the resulting curvature vectors. These invariant shape descriptors provide an embedding into a fixed-dimensional feature space that can be utilized for various applications: (i) as input features for deep and shallow learning techniques; (ii) as input for dimension reduction schemes for providing a visual reference for clustering collection of shapes. The capabilities of the proposed framework are demonstrated in the context of visual analysis and unsupervised classification of histology images.Item Visual Analysis of Multivariate Intensive Care Surveillance Data(The Eurographics Association, 2020) Brich, Nicolas; Schulz, Christoph; Peter, Jörg; Klingert, Wilfried; Schenk, Martin; Weiskopf, Daniel; Krone, Michael; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata GeorgiaWe present an approach for visual analysis of high-dimensional measurement data with varying sampling rates in the context of an experimental post-surgery study performed on a porcine surrogate model. The study aimed at identifying parameters suitable for diagnosing and prognosticating the volume state-a crucial and difficult task in intensive care medicine. In intensive care, most assessments not only depend on a single measurement but a plethora of mixed measurements over time. Even for trained experts, efficient and accurate analysis of such multivariate time-dependent data remains a challenging task. We present a linked-view post hoc visual analysis application that reduces data complexity by combining projection-based time curves for overview with small multiples for details on demand. Our approach supports not only the analysis of individual patients but also the analysis of ensembles by adapting existing techniques using non-parametric statistics. We evaluated the effectiveness and acceptance of our application through expert feedback with domain scientists from the surgical department using real-world data: the results show that our approach allows for detailed analysis of changes in patient state while also summarizing the temporal development of the overall condition. Furthermore, the medical experts believe that our method can be transferred from medical research to the clinical context, for example, to identify the early onset of a sepsis.Item VR Acrophobia Treatment - Development of Customizable Acrophobia Inducing Scenarios(The Eurographics Association, 2020) Wagner, Sebastian; Illner, Kay; Weber, Matthias; Preim, Bernhard; Saalfeld, Patrick; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata GeorgiaSpecific phobias are among the most common mental diseases, affecting the lives of millions of people. Yet, many cases remain untreated and even undiagnosed, partly due to entry barriers such as waiting times and inconvenience of therapy. To improve the therapeutic options and convenience for the treatment of specific phobias, we implemented a virtual reality application for treating acrophobia (fear of heights) with in-virtuo exposure therapy. Our concept is based on principles from psychology and interaction design. This concept is then implemented using the game engine Unity and Oculus Rift headset as a target device for VR display. Our application has a wide range of customization options, which enables it to be personalized to individual patients. In addition, a number of motivational methods are integrated, which are intended to increase patient motivation, as motivation is essential for a successful therapy.Item VRIDAA: Virtual Reality Platform for Training and Planning Implantations of Occluder Devices in Left Atrial Appendages(The Eurographics Association, 2020) Medina, Elodie; Aguado, Ainhoa M.; Mill, Jordi; Freixa, Xavier; Arzamendi, Dabit; Yagüe, Carlos; Camara, Oscar; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata GeorgiaPersonalized anatomical information of the heart is usually obtained from the visual analysis of patient-specific medical images with standard multiplanar reconstruction (MPR) of 2D orthogonal slices, volume rendering and surface mesh views. Commonly, medical data is visualized in 2D flat screens, thus hampering the understanding of 3D complex anatomical details, including incorrect depth/scaling perception, which is critical for some cardiac interventions such as medical device implantations. Virtual reality (VR) is becoming a valid complementary technology overcoming some of the limitations of conventional visualization techniques and allowing an enhanced and fully interactive exploration of human anatomy. In this work, we present VRIDAA, a VR-based platform for the visualization of patient-specific cardiac geometries and the virtual implantation of left atrial appendage occluder (LAAO) devices. It includes different visualization and interaction modes to jointly inspect 3D LA geometries and different LAAO devices, MPR 2D imaging slices, several landmarks and morphological parameters relevant to LAAO, among other functionalities. The platform was designed and tested by two interventional cardiologists and LAAO researchers, obtaining very positive user feedback about its potential, highlighting VRIDAA as a source of motivation for trainees and its usefulness to better understand the required surgical approach before the intervention.