VCBM 2020: Eurographics Workshop on Visual Computing for Biology and Medicine

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https://diglib.eg.org/handle/10.2312/2632935

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Student and Teacher Meet in a Shared Virtual Reality: A one-on-one Tutoring System for Anatomy Education
(The Eurographics Association, 2020) Saalfeld, Patrick; Schmeier, Anna; D'Hanis, Wolfgang; Rothkötter, Hermann-Josef; Preim, Bernhard; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
We introduce a Virtual Reality (VR) one-on-one tutoring system to support anatomy education. A student uses a fully immersive VR headset to explore the anatomy of the base of the human skull. A teacher guides the student by using the semi-immersive zSpace. Both systems are connected via network and each action is synchronized between both systems. The teacher is provided with various features to direct the student through the immersive learning experience. The teacher can influence the student's navigation or provide annotations on the fly and hereby improve the student's learning experience.
Learning Hand Anatomy with Sense of Embodiment
(The Eurographics Association, 2020) Saalfeld, Patrick; Albrecht, Aylin; D'Hanis, Wolfgang; Rothkötter, Hermann-Josef; Preim, Bernhard; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
We present a VR-based prototype for learning the hand anatomy. The prototype is designed to support embodied cognition, i.e., a learning process based on movements. The learner employs the prototype in VR by moving their own hand and fingers and observing how the virtual anatomical hand model mirrors this movement. The display of anatomical systems and their names can be adjusted. The prototype is deployed on the Oculus Quest and uses its native hand tracking capabilities to obtain the hand posture of the user. The potential of the prototype is shown with a small user study.
Real-Time Visualization of 3D Amyloid-Beta Fibrils from 2D Cryo-EM Density Maps
(The Eurographics Association, 2020) Kniesel, Hannah; Ropinski, Timo; Hermosilla, Pedro; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
Amyloid-beta fibrils are the result of the accumulation of misfolded amyloid precursor proteins along an axis. These fibrils play a crucial role in the development of Alzheimer's disease, and yet its creation and structure are not fully understood. Visualization is often used to understand the structure of such fibrils. Unfortunately, existing algorithms require high memory consumption limiting their applications. In this paper, we introduce a ray marching algorithm that takes advantage of the inherent repetition in these atomic structures, requiring only a 2D density map to represent the fibril. During ray marching, the texture coordinates are transformed based on the position of the sample along the longitudinal axis, simulating the rotation of the fibrils. Our algorithm reduces memory consumption by a large margin and improves GPU cache hits, making it suitable for real-time visualizations. Moreover, we present several shading algorithms for this type of data, such as shadows or ambient occlusion, in order to improve perception. Lastly, we provide a simple yet effective algorithm to communicate the uncertainty introduced during reconstruction. During the evaluation process, we were able to show, that our approach not only outperforms the Standard Volume Rendering method by significantly lower memory consumption and high image quality for low resolution 2D density maps but also in performance.
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 Georgia
We 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.
Aneulysis - A System for Aneurysm Data Analysis
(The Eurographics Association, 2020) Meuschke, Monique; Wickenhöfer, Ralph; Preim, Bernhard; Lawonn, Kai; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
We present ANEULYSIS, a system to improve risk assessment and treatment planning of cerebral aneurysms. Aneurysm treatment must be carefully examined as there is a risk of fatal outcome during surgery. Aneurysm growth, rupture, and treatment success depend on the interplay of vascular morphology and hemodynamics. Blood flow simulations can obtain the patient-specific hemodynamics. However, analyzing the time-dependent, multi-attribute data is time-consuming and error-prone. ANEULYSIS supports the analysis and visual exploration of aneurysm data including morphological and hemodynamic attributes. Since this is an interdisciplinary process involving both physicians and fluid mechanics experts, we provide a redundancy-free management of aneurysm data sets according to a consistent structure. Major contributions are an improved analysis of morphological aspects, simultaneous evaluation of wall- and flow-related characteristics as well as multiple attributes on the vessel wall, the assessment of mechanical wall processes as well as an automatic classification of the internal flow behavior. It was designed and evaluated in collaboration with domain experts who confirmed its usefulness and clinical necessity.
An Endoscope Interface for Immersive Virtual Reality
(The Eurographics Association, 2020) John, Nigel W.; Day, Thomas W.; Wardle, Terrence; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
This is a work in progress paper that describes a novel endoscope interface designed for use in an immersive virtual reality surgical simulator. We use an affordable off the shelf head mounted display to recreate the operating theatre environment. A hand held controller has been adapted so that it feels like the trainee is holding an endoscope controller with the same functionality. The simulator allows the endoscope shaft to be inserted into a virtual patient and pushed forward to a target position. The paper describes how we have built this surgical simulator with the intention of carrying out a full clinical study in the near future.
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.
GLANCE: Visual Analytics for Monitoring Glaucoma Progression
(The Eurographics Association, 2020) Brandt, Astrid van den; Christopher, Mark; Zangwill, Linda M.; Rezapour, Jasmin; Bowd, Christopher; Baxter, Sally L.; Welsbie, Derek S.; Camp, Andrew; Moghimi, Sasan; Do, Jiun L.; Weinreb, Robert N.; Snijders, Chris C. P.; Westenberg, Michel A.; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
Deep learning is increasingly used in the field of glaucoma research. Although deep learning models can achieve high accuracy, issues with trust, interpretability, and practical utility form barriers to adoption in clinical practice. In this study, we explore whether and how visualizations of deep learning-based measurements can be used for glaucoma management in the clinic. Through iterative design sessions with ophthalmologists, vision researchers, and manufacturers of optical coherence tomography (OCT) instruments, we distilled four main tasks, and designed a visualization tool that incorporates a visual field (VF) prediction model to provide clinical decision support in managing glaucoma progression. The tasks are: (1) assess reliability of a prediction, (2) understand why the model made a prediction, (3) alert to features that are relevant, and (4) guide future scheduling of VFs. Our approach is novel in that it considers utility of the system in a clinical context where time is limited. With use cases and a pilot user study, we demonstrate that our approach can aid clinicians in clinical management decisions and obtain appropriate trust in the system. Taken together, our work shows how visual explanations of automated methods can augment clinicians' knowledge and calibrate their trust in DL-based measurements during clinical decision making.
Interactive Classification of Multi-Shell Diffusion MRI With Features From a Dual-Branch CNN Autoencoder
(The Eurographics Association, 2020) Torayev, Agajan; Schultz, Thomas; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
Multi-shell diffusion MRI and Diffusion Spectrum Imaging are modern neuroimaging modalities that acquire diffusion weighted images at a high angular resolution, while also probing varying levels of diffusion weighting (b values). This yields large and intricate data for which very few interactive visualization techniques are currently available. We designed and implemented the first system that permits an interactive, iteratively refined classification of such data, which can serve as a foundation for isosurface visualizations and direct volume rendering. Our system leverages features learned by a Convolutional Neural Network. CNNs are state of the art for representation learning, but training them is too slow for interactive use. Therefore, we combine a computationally efficient random forest classifier with autoencoder based features that can be pre-computed by the CNN. Since features from existing CNN architectures are not suitable for this purpose, we design a specific dual-branch CNN architecture, and carefully evaluate our design decisions. We demonstrate that our approach produces more accurate classifications compared to learning with raw data, established domain-specific features, or PCA dimensionality reduction.
The Virtual Reality Flow Lens for Blood Flow Exploration
(The Eurographics Association, 2020) Behrendt, Benjamin; Piotrowski, Lisa; Saalfeld, Sylvia; Preim, Bernhard; Saalfeld, Patrick; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
The exploration of time-dependent measured or simulated blood flow is challenging due to the complex three-dimensional structure of vessels and blood flow patterns. Especially on a 2D screen, understanding their full shape and interacting with them is difficult. Critical regions do not always stand out in the visualization and may easily be missed without proper interaction and filtering techniques. The FlowLens [GNBP11] was introduced as a focus-and-context technique to explore one specific blood flow parameter in the context of other parameters for the purpose of treatment planning. With the recent availability of affordable VR glasses it is possible to adapt the concepts of the FlowLens into immersive VR and make them available to a broader group of users. Translating the concept of the Flow Lens to VR leads to a number of design decisions not only based around what functions to include, but also how they can be made available to the user. In this paper, we present a configurable focus-and-context visualization for the use with virtual reality headsets and controllers that allows users to freely explore blood flow data within a VR environment. The advantage of such a solution is the improved perception of the complex spatial structures that results from being surrounded by them instead of observing through a small screen.
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 Georgia
Frequency 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.
VCBM 2020: Frontmatter
(The Eurographics Association, 2020) Kozlíková, Barbora; Krone, Michael; Smit, Noeska; Nieselt, Kay; Raidou, Renata Georgia; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
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 Georgia
Personalized 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.
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 Georgia
We 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.
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 Georgia
Many 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.
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 Georgia
Specific 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.
VirtualDSA++: Automated Segmentation, Vessel Labeling, Occlusion Detection and Graph Search on CT-Angiography Data
(The Eurographics Association, 2020) Thamm, Florian; Jürgens, Markus; Ditt, Hendrik; Maier, Andreas; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
Computed Tomography Angiography (CTA) is one of the most commonly used modalities in the diagnosis of cerebrovascular diseases like ischemic strokes. Usually, the anatomy of interest in ischemic stroke cases is the Circle of Willis and its peripherals, the cerebral arteries, as these vessels are the most prominent candidates for occlusions. The diagnosis of occlusions in these vessels remains challenging, not only because of the large amount of surrounding vessels but also due to the large number of anatomical variants. We propose a fully automated image processing and visualization pipeline, which provides a full segmentation and modelling of the cerebral arterial tree for CTA data. The model itself enables the interactive masking of unimportant vessel structures e.g. veins like the Sinus Sagittalis, and the interactive planning of shortest paths meant to be used to prepare further treatments like a mechanical thrombectomy. Additionally, the algorithm automatically labels the cerebral arteries (Middle Cerebral Artery left and right, Anterior Cerebral Artery short, Posterior Cerebral Artery left and right) detects occlusions or interruptions in these vessels. The proposed pipeline does not require a prior non-contrast CT scan and achieves a comparable segmentation appearance as in a Digital Subtraction Angiography (DSA).
Uncertainty-aware Brain Lesion Visualization
(The Eurographics Association, 2020) Gillmann, Christina; Saur, Dorothee; Wischgoll, Thomas; Hoffmann, Karl-Titus; Hagen, Hans; Maciejewski, Ross; Scheuermann, Gerik; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
A brain lesion is an area of tissue that has been damaged through injury or disease. Its analysis is an essential task for medical researchers to understand diseases and find proper treatments. In this context, visualization approaches became an important tool to locate, quantify, and analyze brain lesions. Unfortunately, image uncertainty highly effects the accuracy of the visualization output. These effects are not covered well in existing approaches, leading to miss-interpretation or a lack of trust in the analysis result. In this work, we present an uncertainty-aware visualization pipeline especially designed for brain lesions. Our method is based on an uncertainty measure for image data that forms the input of an uncertainty-aware segmentation approach. Here, medical doctors can determine the lesion in the patient's brain and the result can be visualized by an uncertainty-aware geometry rendering. We applied our approach to two patient datasets to review the lesions. Our results indicate increased knowledge discovery in brain lesion analysis that provides a quantification of trust in the generated results.

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