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In dieser Arbeit entwickeln wir ein visuelles Analysesystem, das die Vorhersage, Analyse und Kommunikation der Ergebnisse von COVID-19-Krankenhausaufenthalten unterstützt. Obwohl mehrere reale Datensätze über COVID-19 öffentlich verfügbar sind, konzentriert sich der Großteil der aktuellen Forschung auf die Erkennung der Krankheit in Röntgenbildern der Brust. Bislang gibt es keine Arbeit, die Erkenntnisse aus medizinischen Bilddaten mit denen aus klinischen Daten kombiniert und die Wahrscheinlichkeit eines Aufenthalts auf der Intensivstation, einer Beatmung oder eines Todesfalls vorhersagt. Darüber hinaus hat sich die bisherige Forschung noch nicht auf die Vermittlung der Ergebnisse an die breite Öffentlichkeit konzentriert. Um die Vorhersage, Analyse und Kommunikation der Ergebnisse von COVID-19-Krankenhausaufenthalten auf der rundlage eines öffentlich zugänglichen Datensatzes zu unterstützen, der sowohl elektronische Gesundheitsdaten als auch medizinische Bilddaten umfasst, führen wir die folgenden drei Schritte durch: (1) automatisierte Segmentierung der verfügbaren Röntgenbilder und Verarbeitung der klinischen Daten, (2) Entwicklung eines Modells für die Vorhersage von Krankheitsverläufen und Vergleich mit modernsten Vorhersagewerten für beide Datenquellen, medizinische Bilder und klinische Daten, und (3) die Kommunikation er Ergebnisse für drei verschiedene Nutzergruppen (medizinische und klinische Experten, Experten für Datenanalyse und die allgemeine Bevölkerung) über ein interaktives Dashboard. Das Dashboard ist so konzipiert, dass die Benutzer benutzerspezifische Aufgaben lösen können, die ebenfalls in dieser Arbeit definiert wurden. Vorläufige Ergebnisse deuten darauf hin, dass die Vorhersageergebnisse durch die Kombination von medizinischen Bilddaten mit klinischen Daten verbessert werden, während sich die Analyse und Kommunikation von Krankenhausaufenthaltsergebnissen als ein breites und bedeutendes Thema im Rahmen der COVID-19-Prävention erweist., We propose a visual analytics framework to support the prediction, analysis, and communication of COVID-19 hospitalization outcomes. Although several real-world data sets about COVID-19 are openly available, most of the current research focuses on the detection of the disease through chest X-ray images. Until now, no previous work exists on combining insights from medical image data with knowledge extracted from clinical data, predicting the likelihood of an intensive care unit (ICU) visit, ventilation, or decease. Moreover, available literature has not yet focused on communicating such results to the broader society. To support the prediction, analysis, and communication of the outcomes of COVID-19 hospitalizations on the basis of a publicly available data set comprising both electronic health data and medical image data Saltz et al. [2021], we conduct the following three steps: (1) automated segmentation of the available X-ray images and processing of clinical data, (2) development of a model for the prediction of disease outcomes and a comparison to state-of-the-art prediction scores for both data sources, i.e., medical images and clinical data, and (3) the communication of outcomes to three different user groups (namely, medical and clinical experts, experts in data analytics, and the general population) through an interactive dashboard. The dashboard is designed to enable users to solve user-specific tasks, also defined in this work. Preliminary results indicate that the prediction results are improved by combining medical image data with clinical data, while analysis and communication of hospitalization outcomes prove to be a wide and significant topic in the context of COVID-19 prevention.

We present an approach for computing camera animations composed of optimal views to support the visual exploration of blood flow data using cerebral aneurysms as major example. Medical researchers are interested in hemodynamic parameters and vessel wall characteristics. The time-dependent character of blood flow data complicates the visual analysis. Our approach is modeled as an optimization problem to automatically determine camera paths during the cardiac cycle. These consist of optimal viewpoints showing regions with suspicious characteristics of wall- and flow-related parameters. This provides medical researchers with an efficient method of obtaining a fast overview of patient-specific blood flow data., Eurographics Workshop on Visual Computing for Biology and Medicine, The path that blood takes, 101, 105, Vikram Apilla, Benjamin Behrendt, Kai Lawonn, Bernhard Preim, and Monique Meuschke, CCS Concepts: Applied computing --> Life and medical sciences; Human-centered computing --> Visualization

Computational Workflows are widely used in data analysis, enabling innovation and decision-making. In many domains (bioinformatics, image analysis, & radio astronomy) the analysis components are numerous and written in multiple different computer languages by third parties. However, many competing workflow systems exist, severely limiting portability of such workflows, thereby hindering the transfer of workflows between different systems, between different projects and different settings, leading to vendor lock-ins and limiting their generic re-usability. Here we present the Common Workflow Language (CWL) project which produces free and open standards for describing command-line tool based workflows. The CWL standards provide a common but reduced set of abstractions that are both used in practice and implemented in many popular workflow systems. The CWL language is declarative, which allows expressing computational workflows constructed from diverse software tools, executed each through their command-line interface. Being explicit about the runtime environment and any use of software containers enables portability and reuse. Workflows written according to the CWL standards are a reusable description of that analysis that are runnable on a diverse set of computing environments. These descriptions contain enough information for advanced optimization without additional input from workflow authors. The CWL standards support polylingual workflows, enabling portability and reuse of such workflows, easing for example scholarly publication, fulfilling regulatory requirements, collaboration in/between academic research and industry, while reducing implementation costs. CWL has been taken up by a wide variety of domains, and industries and support has been implemented in many major workflow systems., 8 pages, 3 figures. For the LaTex source code of this paper, see https://github.com/mr-c/cwl_methods_included

Clinical decision-making for complex diseases such as cancer aims at finding the right diagnosis, optimal treatment or best aftercare for a specific patient. The decision-making process is very challenging due to the distributed storage of patient information entities in multiple hospital information systems, the required inclusion of multiple clinical disciplines with their different views of disease and therapy, and the multitude of available medical examinations, therapy options and aftercare strategies. Clinical Decision Support Systems (CDSS) address these difficulties by presenting all relevant information entities in a concise manner and providing a recommendation based on interdisciplinary disease- and patient-specific models of diagnosis and treatment. This work summarizes our research on visual assistance for therapy decision-making. We aim at supporting the preparation and implementation of expert meetings discussing cancer cases (tumor boards) and the aftercare consultation. In very recent work, we started to address the generation of models underlying a CDSS. The developed solutions combine state-of-the-art interactive visualizations with methods from statistics, machine learning and information organization., EuroVis 2021 - Dirk Bartz Prize, 2nd Prize, 7, 11, Juliane Müller, Mario Cypko, Alexander Oeser, Matthäus Stoehr, Veit Zebralla, Stefanie Schreiber, Susanne Wiegand, Andreas Dietz, and Steffen Oeltze-Jafra, CCS Concepts: Human-centered computing --> Visual analytics; Applied computing --> Life and medical sciences

The visual communication of biomedical processes draws from diverse techniques in both visualization and biomedical illustration. However, matching these techniques to their intended audience often relies on practice-based heuristics or narrow-scope evaluations. We present an exploratory study of the criteria that audiences use when evaluating a biomedical process visualization targeted for communication. Designed over a series of expert interviews and focus groups, our study focuses on common communication scenarios of five well-known biomedical processes and their standard visual representations. We framed these scenarios in a survey with participant expertise spanning from minimal to expert knowledge of a given topic. Our results show frequent overlap in abstraction preferences between expert and non-expert audiences, with similar prioritization of clarity and the ability of an asset to meet a given communication objective. We also found that some illustrative conventions are not as clear as we thought, e.g., glows have broadly ambiguous meaning, while other approaches were unexpectedly preferred, e.g., biomedical illustrations in place of data-driven visualizations. Our findings suggest numerous opportunities for the continued convergence of visualization and biomedical illustration techniques for targeted visualization design., Eurographics Workshop on Visual Computing for Biology and Medicine, Getting an overview in bio (and medicine), 1, 12, Laura Garrison, Monique Meuschke, Jennifer Fairman, Noeska N. Smit, Bernhard Preim, and Stefan Bruckner, CCS Concepts: Human-centered computing --> Visualization design and evaluation methods; Scientific visualization; Visualization theory, concepts and paradigms; Computer Applications --> Life and Medical Sciences

Real-world sculptures that display patient imaging data for anatomical education purposes have seen a recent resurgence through the field of data physicalization. In this paper, we describe an automated process for the computer-assisted generation of sculptures that can be employed for anatomical education among the general population. We propose a workflow that supports non-expert users to generate and physically display volumetric medical data in a visually appealing and engaging way. Our approach generates slide-based, interactive sculptures-called volograms-that resemble holograms of underlying medical data. The volograms are made out of affordable and readily available materials (e.g., transparent foils and cardboard) and can be produced through commonly available means. To evaluate the educational value of the proposed approach with our target audience, we assess the volograms, as opposed to classical, on-screen medical visualizations in a user study. The results of our study, while highlighting current weaknesses of our physicalization, also point to interesting future directions., Eurographics Workshop on Visual Computing for Biology and Medicine, Getting an overview in bio (and medicine), 19, 23, Daniel Pahr, Hsiang-Yun Wu, and Renata Georgia Raidou, CCS Concepts: Applied computing --> Life and medical sciences; Human-centered computing --> Scientific visualization; Visualization systems and tools

In this thesis, the problem of predicting the full dose distribution from a partially modeled dose calculation is addressed. Two solutions were studied: a vanilla Hierarchically Densely Connected U-net (HDUnet) and a Conditional Generative Adversarial Network (CGAN) with HDUnet as a generator. The CGAN approach is a 3D version of Pix2Pix [1] for Image to Image translation which we name Dose2Dose. The research question that this project tackled is whether the Dose2Dose can learn more effective dose transformations than the vanilla HDUnet. To answer this, the models were trained using dose calculations of phantom slabs generated for the problem in pairs of inputs (doses without magnetic field) and targets (doses with magnetic field). Once trained, the models were evaluated and compared in various aspects. The evidence gathered suggests that the vanilla HDUnet model can learn to generate better dose predictions than the generative model. However, in terms of the resulting dose distributions, the samples generated from the Dose2Dose are as likely to belong to the target dose calculation distribution as those of the vanilla HDUnet. The results contain errors of considerable magnitude, and do not accomplish clinical suitability tests. I denna avhandling har problemet med att förutsäga full dosfördelning från en delvis modellerad dosberäkning tagits upp. Två lösningar studerades: ett vanilla HDUnet och ett betingat generativt nätverk (CGAN) med HDUnet som generator. CGAN -metoden var en 3D-version av Pix2Pix [1] för översättning av bild till bild med namnet Dose2Dose. Forskningsfrågan som detta projekt tog upp var om Dose2Dose kan lära sig mer effektiva dostransformationer än vanilla HDUnet. För att svara på detta tränades modellerna med hjälp av parvisa dosberäkningar, i indata (doser utan magnetfält) och mål (doser med magnetfält).. När de var tränade utvärderades modellerna och jämfördes i olika aspekter. De samlade bevisen tyder på att Vanilla HDUnet -modellen kan lära sig att generera bättre dosförutsägelser än den generativa modellen. När det gäller de resulterande dosfördelningarna är emellertid de prover som genererats från Dose2Dose lika sannolikt att tillhöra måldosberäkningsfördelningen som de för vanilla HDUnet. Resultaten innehåller stora storleksfel och uppfyller inte kraven för klinisk tillämpbarhet.

Augmented and virtual reality (XR) are potentially powerful tools for enhancing the efficiency of interactive visualization of complex data in biology and medicine. The benefits of visualization of digital objects in XR mainly arise from enhanced depth perception due to the stereoscopic nature of XR head mounted devices. With the added depth dimension, XR is in a prime position to convey complex information and support tasks where 3D information is important. In order to inform the development of novel XR applications in the biology and medicine domain, we present a survey which reviews the neuroscientific basis underlying the immersive features of XR. To make this literature more accessible to the visualization community, we first describe the basics of the visual system, highlighting how visual features are combined to objects and processed in higher cortical areas with a special focus on depth vision. Based on state of the art findings in neuroscience literature related to depth perception, we provide several recommendations for developers and designers. Our aim is to aid development of XR applications and strengthen development of tools aimed at molecular visualization, medical education, and surgery, as well as inspire new application areas., Eurographics Workshop on Visual Computing for Biology and Medicine, Let's look into your brains, 37, 54, Vetle Hushagen, Gustav C. Tresselt, Noeska N. Smit, and Karsten Specht, CCS Concepts: General and reference --> Surveys and overviews; Applied computing --> Life and medical sciences; Human-centered computing --> Virtual reality; Mixed / augmented reality

4D Flow MRI is a recent promising technology that is able to capture blood flow information within the heart chambers over a cardiac cycle. To accurately study the flow inside the chambers, there is a need for a high quality anatomical reference which can be provided by another scan known as 3D cine MRI (short-axis 3D (multiple 2D slices) cine SSFP). To take advantage of both scans, data fusion can be done using an intensity-based registration. To reduce the impact of noise on the registration result and the chance of misalignment between the organs, defining a region of interest (localization) should be done prior to the registration. Localizing a dataset - especially a time-varying dataset - can be a daunting task since the localization should be provided for all time frames. We design and evaluate different strategies for extending single time frame localization to time varying data in order to register the 4D Flow MRI and 3D cine MRI over the cardiac cycle., Eurographics Workshop on Visual Computing for Biology and Medicine, Shooting rays of sorts through people, 25, 29, Samin Sabokrohiyeh, Kathleen Ang, and Faramarz Samavati, CCS Concepts: Applied computing --> Life and medical sciences

Craniotomy is a procedure where neurosurgeons open the patient's skull to gain direct access to the brain. The craniotomy's position defines the access path from the skull surface to the tumour and, consequently, the healthy brain tissue to be removed to reach the tumour. This is a complex procedure where a neurosurgeon is required to mentally reconstruct spatial relations of important brain structures to avoid removing them as much as possible. We propose a visualisation method using Augmented Reality to assist in the planning of a craniotomy. The goal of this study is to visualise important brain structures aligned with the physical position of the patient and to allow a better perception of the spatial relations of the structures. Additionally, a heat map was developed that is projected on top of the skull to provide a quick overview of the structures between a chosen location on the skull and the tumour. In the experiments, tracking accuracy was assessed, and colour maps were assessed for use in an AR device. Additionally, we conducted a user study amongst neurosurgeons and surgeons from other fields to evaluate the proposed visualisation using a phantom head. Most participants indeed agree that the visualisation can assist in planning a craniotomy and feedback on future improvements towards the clinical scenario was collected., Eurographics Workshop on Visual Computing for Biology and Medicine, Conspiring to cut people open, 135, 144, Joost Wooning, Mohamed Benmahdjoub, Theo van Walsum, and Ricardo Marroquim, CCS Concepts: Human-centered computing --> Scientific visualization; Heat maps; Applied computing --> Life and medical sciences

Pediatric brain tumor radiotherapy research is investigating how radiation influences the development and function of a patient's brain. To better understand how brain growth is affected by the treatment, the brain structures of the patient need to be explored and analyzed pre- and post-treatment. In this way, anatomical changes are observed over a long period and are assessed as potential early markers of cognitive or functional damage. In this early work, we propose an automated approach for the visual assessment of the growth prediction of brain structures in pediatric brain tumor radiotherapy patients. Our approach reduces the need for re-segmentation and the time required for it. We employ as a basis pre-treatment Computed Tomography (CT) scans with manual delineations (i.e., segmentation masks) of specific brain structures of interest. These pre-treatment masks are used as initialization, to predict the corresponding masks on multiple post-treatment follow-up Magnetic Resonance (MR) images, using an active contour model approach. For the accuracy quantification of the automatically predicted posttreatment masks, a support vector regressor (SVR) with features related to geometry, intensity, and gradients is trained on the pre-treatment data. Finally, a distance transform is employed to calculate the distances between pre- and post-treatment data and to visualize the predicted growth of a brain structure, along with its respective accuracy. Although segmentations of larger structures are more accurately predicted, the growth behavior of all structures is learned correctly, as indicated by the SVR results. This suggests that our pipeline is a positive initial step for the visual assessment of brain structure growth prediction., Eurographics Workshop on Visual Computing for Biology and Medicine, Shooting rays of sorts through people, 31, 35, Caroline Magg, Laura Toussaint, Ludvig P. Muren, Danny J. Indelicato, and Renata Georgia Raidou, CCS Concepts: Applied computing --> Life and medical sciences; Human-centered computing --> Visualization

This work is part of an experimental project aiming at understanding the kinetics of human gastric emptying. For this purpose magnetic resonance imaging (MRI) images of the stomach of healthy volunteers have been acquired using a state-of-art scanner with an adapted protocol. The challenge is to follow the stomach content (food) in the data. Frozen garden peas and petits pois have been chosen as experimental proof-of-concept as their shapes are well defined and are not altered in the early stages of digestion. The food recognition is performed as a binary classification implemented using a deep convolutional neural network (CNN). Input hyperparameters, here image size and number of epochs, were exhaustively evaluated to identify the combination of parameters that produces the best classification. The results have been analysed using interactive visualisation. We prove in this paper that advances in computer vision and machine learning can be deployed to automatically label the content of the stomach even when the amount of training data is low and the data imbalanced. Interactive visualisation helps identify the most effective combinations of hyperparameters to maximise accuracy, precision, recall and F1 score, leaving the end-user evaluate the possible trade-off between these metrics. Food recognition in MRI scans through neural network produced an accuracy of 0.97, precision of 0.91, recall of 0.86 and F1 score of 0.89, all close to 1., Computer Graphics and Visual Computing (CGVC), Visualisation and Machine Learning, 11, 18, Joshua Gardner, Shatha Al-Maliki, Évelyne Lutton, François Boué, and Franck P. Vidal, CCS Concepts: Applied computing --> Life and medical sciences; Computing methodologies --> Machine learning

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., Eurographics Workshop on Visual Computing for Biology and Medicine, Vascular and Flow, 127, 138, Monique Meuschke, Ralph Wickenhöfer, Bernhard Preim, and Kai Lawonn, CCS Concepts: Computing methodologies --> Computer Graphics; Computer Applications --> Life and Medical Sciences

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., Eurographics Workshop on Visual Computing for Biology and Medicine, Feature Analysis, 13, 24, Martin Falk, Patric Ljung, Claes Lundström, Anders Ynnerman, and Ingrid Hotz, CCS Concepts: Human-centered computing --> Scientific visualization; Visualization techniques; Applied computing --> Life and medical sciences

Amblyopia is a neurological impairment where children suffer from the insufficient visual gathering. Recent studies investigated the ability to improve amblyopic children's visual acuity. However, according to previous theories, visual acuity is the foundation for an excellent visual system, but more lies beyond acuity improvements, such as their visual cognition and perceptual skills. Virtual Reality acts as a serious game medium for active learning of virtual depth perceptions built after a solid improvement for visual acuity. This paper aims to provide a pediatric-centered practical approach for amblyopic children. We designed a game system in the form of a virtual depth hierarchical game pyramid based upon the classical visual perception hierarchical pyramid and standardized procedures for perceptual training. The bottom-up setup gradually trains for each level up the VR-designed hierarchy while building up a solid foundation., ICAT-EGVE 2020 - International Conference on Artificial Reality and Telexistence and Eurographics Symposium on Virtual Environments, Mixed Reality Applications, 37, 41, Yui Lo, Shufang Tan, Qinglan Shan, Yutong Wang, Mao Yan Chen, Xiaomei Nie, Qinggan Zeng, and Yuhan Dong, CCS Concepts: Human-centered computing --> Interaction design; Applied computing --> Life and medical sciences; Education

Cancer is a complex and dynamic disease manifesting in ~100 distinct cancer types that arise in multiple cell types and organs due to different but related mechanisms. Research from the last decade has revealed vast heterogeneity within and between cancer types, hampering effective treatment and calling for more personalized treatment strategies. This thesis develops methodology for detection and molecular characterization of cancer subtypes by focusing on the analysis of experiments generating large datasets. The first objective was to provide algorithms for rapid detection and quantification of microRNAs and analysis and visualization of DNA methylation. The second objective was to investigate the cellular and molecular origin of embryonal rhabdomyosarcoma (ERMS), a rare and aggressive childhood cancer. Two new computational methods were implemented and evaluated by comparison to previously published findings. DNA copy number alterations and gene expression estimates were obtained from a novel model system for ERMS and integrated with molecular data from cancer patients. Cell tracing experiments unambiguously demonstrated that ERMS is derived from tissue-resident muscle stem cells, at least in the model system used. In-depth data analysis revealed a diverse molecular basis of ERMS, confirming cancer heterogeneity. Surprisingly, activation of zygotic Dux factors identified a novel cancer subtype that is not limited to ERMS, but occurs in a broad range of different human cancer. Based on the results, it can be concluded that computational methods and integrative data analysis are useful to delineate the origin of cancer subtypes and provide a valuable starting point for selection of relevant therapeutic targets. However, future research is needed to establish more holistic analysis approaches and transfer findings into existing clinical routines.

Point mutations of the fibroblast growth factor receptor (FGFR)2 receptor in intrahepatic cholangiocarcinoma (iCC) are mainly of unknown functional significance compared to FGFR2 fusions. Pemigatinib, a tyrosine kinase inhibitor, is approved for the treatment of cholangiocarcinoma with FGFR2 fusion/rearrangement. Although it is hypothesized that FGFR2 mutations may cause uncontrolled activation of the signaling pathway, the data for targeted therapies for FGFR2 mutations remain unclear. In vitro analyses demonstrated the importance of the p.C382R mutation for ligand-independent constitutive activation of FGFR2 with transforming potential. The following report describes the clinical case of a patient diagnosed with an iCC carrying a FGFR2 p.C382R point mutation which was detected in liquid, as well as in tissue-based biopsies. The patient was treated with pemigatinib, resulting in a sustained complete functional remission in fluorodeoxyglucose-positron emission tomography/computed tomography over 10 months to date. The reported case is the first description of a complete functional remission under the treatment with pemigatinib in a patient with p.C383R mutation.

In this preliminary study, we propose visual biofeedback techniques for representing compensatory movements that are commonly found in upper limb rehabilitation exercises. Here, visual biofeedback is represented by stick figures adorned with different graphical elements to highlight abnormal motor patterns. We explore 4 visual biofeedback techniques for analysing movements designed for neuromotor rehabilitation of the upper limb. Co-design sessions were conducted next to 5 rehabilitation professionals. The resulting visual designs were then evaluated by 3 other physiotherapists, each evaluated the visual biofeedback of two types of compensatory movements: arm elevation-flexion and cephalic tilt. Results indicate that although there is a preferred technique, participants suggested to design a novel representation that should incorporate features from different sources, thus designing a hybrid visual biofeedback technique., EuroVis 2019 - Posters, Posters, 33, 35, Daniel S. Lopes, Afonso Faria, Ana Barriga, Sérgio Caneira, Filomena Baptista, Catarina Matos, Ana F. Neves, Leonor Prates, Ângela Maria Pereira, and Hugo Nicolau, CCS Concepts: Human-centered computing --> Empirical studies in HCI; Applied computing --> Life and medical sciences

When Diffusion Tensor Imaging (DTI) is used in clinical studies, statistical hypothesis testing is the standard approach to establish significant differences between groups, such as patients and healthy controls. However, diffusion tensors contain six degrees of freedom, and the most commonly used univariate tests reduce them to a single scalar, such as Fractional Anisotropy. Multivariate tests that account for the full tensor information have been developed, but have not been widely adopted in practice. Based on analyzing the limitations of existing univariate and multivariate tests, we argue that it is beneficial to use a more flexible, steerable test. Therefore, we introduce a test that can be customized to include any subset of tensor attributes that are relevant to the analysis task at hand. We also present a visual analytics system that supports the exploratory task of customizing it to a specific scenario. Our system closely integrates quantitative analysis with suitable visualizations. It links spatial and abstract views to reveal clusters of strong differences, to relate them to the affected anatomical structures, and to visually compare the results of different tests. A use case is presented in which our system leads to the formation of several new hypotheses about the effects of systemic lupus erythematosus on water diffusion in the brain., Eurographics Workshop on Visual Computing for Biology and Medicine, Visual Computing for MRI-based Data, 33, 43, Amin Abbasloo, Vitalis Wiens, Tobias Schmidt-Wilcke, Pia Sundgren, Reinhard Klein, and Thomas Schultz, CCS Concepts: Visualization application domains --> Visual analytics; Life and medical sciences --> Health informatics

In mitral valve interventions, surgeons have to select an optimal combination of techniques for every patient. Especially less experienced physicians would benefit from decision support for this process. To support the visual analysis of the patientspecific valvular dynamics and an in-silico pre-intervention simulation of different therapy options, a real-time simulation of the mitral valve is needed, especially for the use in a time-constrained clinical environment. We develop a simplified model of the mitral valve and propose a novel approach to simulate the mitral valve with position-based dynamics. As input, a mesh representation of the open-state mitral valve, two polygons representing the open and closed annulus states, simplified chordae tendineae, and a set of forces for approximating the surrounding blood are required. The mitral valve model can be deformed to simulate the closing and opening as well as incorporate changes caused by virtual interventions in the simulation. For evaluation, ten mitral valves were reconstructed from transesophageal echocardiogram sequences of patients with normal and abnormal physiology. Experts in cardiac surgery annotated anatomical landmarks for valve reconstruction. The simulation results for closing the valve were qualitatively compared to the anatomy depicted in the image sequences and, if present, the reproduction of a prolapse was verified. In addition, two virtual interventions (annuloplasty and clipping) were performed for one case and provided new insights about changes in valve closure and orifice area after modification. Each simulation ran at interactive frame rates. Our approach enables an efficient simulation of the mitral valve with normal and abnormal valve closing behavior as well as virtual interventions. The simulation results showed good agreements with the image data in general and reproduced valve closure in all cases. In three cases, prolapse was not or not correctly reproduced. Further research is needed to parameterize the model in pathologic cases., Eurographics Workshop on Visual Computing for Biology and Medicine, Animation, Tracking, and Simulations, 165, 175, Lars Walczak, Joachim Georgii, Lennart Tautz, Mathias Neugebauer, Isaac Wamala, Simon Sündermann, Volkmar Falk, and Anja Hennemuth, CCS Concepts: Applied computing --> Life and medical sciences; Health informatics

We present a novel immersive environment for the exploratory analysis of nanoscale cellular reconstructions of rodent brain samples acquired through electron microscopy. The system is focused on medial axis representations (skeletons) of branched and tubular structures of brain cells, and it is specifically designed for: i) effective semi-automatic creation of skeletons from surface-based representations of cells and structures, ii) fast proofreading, i.e., correcting and editing of semi-automatically constructed skeleton representations, and iii) useful exploration, i.e., measuring, comparing, and analyzing geometric features related to cellular structures based on medial axis representations. The application runs in a standard PC-tethered virtual reality (VR) setup with a head mounted display (HMD), controllers, and tracking sensors. The system is currently used by neuroscientists for performing morphology studies on sparse reconstructions of glial cells and neurons extracted from a sample of the somatosensory cortex of a juvenile rat., Smart Tools and Apps for Graphics - Eurographics Italian Chapter Conference, Full Papers, 21, 30, Daniya Boges, Corrado Calì, Pierre J. Magistretti, Markus Hadwiger, Ronell Sicat, and Marco Agus, CCS Concepts: Applied computing --> Life and medical sciences; Imaging; Human-centered computing --> Pointing; Interaction design

Publication :PBio 2018: Proceedings of the 6th International Workshop on Parallelism in Bioinformatics [Abstract] Optimization problems appears in different areas of science and engineering. This paper considers the general problem of reverse engineering in computational biology by means of mixed-integer nonlinear dynamic optimization (MIDO). Although this kind of problems are typically hard, solutions can be achieved for rather complex networks by applying global optimization metaheuristics. The main objective of this work is to handle them by means of multimethod optimization, in which different metaheuristics cooperate to outperform the results obtained by any of them isolated. For its preliminary evaluation we consider a synthetic signaling pathway case study and we assess the performance of the proposal on a public cloud. These results open up new possibilities for other MIDO-based large-scale applications in computational systems biology. Gobierno de España; DPI2017-82896-C2-2-R Gobierno de España; TIN2016-75845-P Xunta de Galicia; R2016/045 Xunta de Galicia; ED431C 2017/04

Muscular activity during human motion is usually quantified by measuring the electrical potential during muscle activation using electromyography (EMG). However, apart from producing electrical activity, muscular contraction of many skeletal muscles also induces subtle deformation of the skin surface. In this paper, we present a method to estimate muscular activation from such 3D skin deformation. To this end, we introduce a capture system that reconstructs the 3D motion of the skin from multi-view video data and simultaneously measures true muscle activity with EMG sensors. Our data reveals strong correlations between the skin deformation and muscular activity during one-leg stances. We propose a pose normalization procedure and a novel model based on Supervised Principal Component Regression that automatically segments individual muscles and estimates their activation from 3D surface deformation. Our evaluation shows that the model generalizes to varying body shapes and that the estimated activation closely fits the measured EMG data., Eurographics Workshop on Visual Computing for Biology and Medicine, Surfaces, 157, 166, Johannes Metzler, Thomas Neumann, Stefanie Gassel, Jens Friedrich, and Markus Wacker, CCS Concepts: Applied computing --> Life and medical sciences