VCBM 2021: Eurographics Workshop on Visual Computing for Biology and Medicine

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2.5D Geometric Mapping of Aortic Blood Flow Data for Cohort Visualization
(The Eurographics Association, 2021) Behrendt, Benjamin; Pleuss-Engelhardt, David; Gutberlet, Matthias; Preim, Bernhard; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
Four-dimensional phase-contrast magnetic resonance imaging (4D PC-MRI) allows for a non-invasive acquisition of timeresolved blood flow measurements, providing a valuable aid to clinicians and researchers seeking a better understanding of the interrelation between pathologies of the cardiovascular system and changes in blood flow patterns. Such research requires extensive analysis and comparison of blood flow data within and between different patient cohorts representing different age groups, genders and pathologies. However, a direct comparison between large numbers of datasets is not feasible due to the complexity of the data. In this paper, we present a novel approach to normalize aortic 4D PC-MRI datasets to enable qualitative and quantitative comparisons. We define normalized coordinate systems for the vessel surface as well as the intravascular volume, allowing for the computation of quantitative measures between datasets for both hemodynamic surface parameters as well as flow or pressure fields. To support the understanding of the geometric deformations involved in this process, individual transformations can not only be toggled on or off, but smoothly transitioned between anatomically faithful and fully abstracted states. In an informal interview with an expert radiologist, we confirm the usefulness of our technique. We also report on initial findings from exploring a database of 138 datasets consisting of both patient and healthy volunteers.
AR-Assisted Craniotomy Planning for Tumour Resection
(The Eurographics Association, 2021) Wooning, Joost; Benmahdjoub, Mohamed; Walsum, Theo van; Marroquim, Ricardo; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
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.
Automatic Animations to Analyze Blood Flow Data
(The Eurographics Association, 2021) Apilla, Vikram; Behrendt, Benjamin; Lawonn, Kai; Preim, Bernhard; Meuschke, Monique; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
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.
Automatic Cutting and Flattening of Carotid Artery Geometries
(The Eurographics Association, 2021) Eulzer, Pepe; Richter, Kevin; Meuschke, Monique; Hundertmark, Anna; Lawonn, Kai; ,; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
We propose a novel method to cut and flatten vascular geometry that results in an intuitive mapping between the 3D and 2D domains. Our approach is fully automatic, and the sole input is the vessel geometry. We aim to simplify parameter analysis on vessel walls for research on vascular disease and computational hemodynamics. We present a use case for the flattening to aid efforts in investigating the pathophysiology of carotid stenoses (vessel constrictions that are a root cause of stroke). To achieve an intuitive mapping, we introduce the notion of natural vessel cuts. They remain on one side of vessel branches, meaning they adhere to the longitudinal direction and thus result in a comprehensible unfolding of the geometry. Vessel branches and endpoints are automatically detected, and a 2D layout configuration is found that retains the original branch layout. We employ a quasi-isometric surface parameterization to map the geometry to the 2D domain as a single patch. The flattened depiction resolves the need for tedious 3D interaction as the whole surface is visible at once.We found this overview particularly beneficial for exploring temporally resolved parameters.
An Exploration of Practice and Preferences for the Visual Communication of Biomedical Processes
(The Eurographics Association, 2021) Garrison, Laura; Meuschke, Monique; Fairman, Jennifer; Smit, Noeska N.; Preim, Bernhard; Bruckner, Stefan; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
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.
Interactive Multimodal Imaging Visualization for Multiple Sclerosis Lesion Analysis
(The Eurographics Association, 2021) Sugathan, Sherin; Bartsch, Hauke; Riemer, Frank; Grüner, Renate; Lawonn, Kai; Smit, Noeska N.; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
Multiple Sclerosis (MS) is a brain disease that is diagnosed and monitored extensively through MRI scans. One of the criteria is the appearance of so-called brain lesions. The lesions show up on MRI scans as regions with elevated or reduced contrast compared to the surrounding healthy tissue. Understanding the complex interplay of contrast, location and shape in images from multiple modalities from 2D MRI slices is challenging. Advanced visualization of appearance- and location-related features of lesions would help researchers in defining better disease characterization through MS research. Since a permanent cure is not possible in MS and medication-based disease modification is a common treatment path, providing better visualizations would strengthen research which investigates the effect of white matter lesions. Here we present an advanced visualization solution that supports analysis from multiple imaging modalities acquired in a clinical routine examination. The solution holds potential for enabling researchers to have a more intuitive perception of lesion features. As an example for enhancing the analytic possibilities, we demonstrate the benefits of lesion projection using both Diffusion Tensor Imaging (DTI) and gradient-based techniques. This approach enables users to assess brain structures across individuals as the atlas-based analysis provides 3D anchoring and labeling of regions across a series of brain scans from the same participant and across different participants. The projections on the brain surface also enable researchers to conduct detailed studies on the relationship between cognitive disabilities and location of lesions. This allows researchers to correlate lesions to Brodmann areas and related brain functions. We realize the solutions in a prototype application that supports both DTI and structural data. A qualitative evaluation demonstrates that our approach supports MS researchers by providing new opportunities for MS research.
Multiple Scale Visualization of Electronic Health Records to Support Finding Medical Narratives
(The Eurographics Association, 2021) Linden, Sanne van der; Wijk, Jarke J. van; Funk, Mathias; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
Electronic Health Records (EHRs) contain rich medical information about patients, possibly hundreds of notes, lab results, images and other information. Doctors can easily be overwhelmed by this wealth of information. For their daily work, they need to derive narratives from all this information to get insights into the main issues of their patients. Standard solutions show all the information in linear lists, often leading to cognitive overload; research solutions provide timelines and relations between the notes but provide too much fragmented information. We propose MEDeNAR, a system for enabling medical professionals to obtain insights from EHRs based on the different tasks in their workflow. The key aspects of our system are the introduction of an intermediate level that summarizes the information using clustering and NLP methods. The results are visualized along a timeline and provide easy access to the detailed descriptions in notes and lab results at the EHR level. We designed the system using an iterative design process in collaboration with 18 doctors, two nurses and 14 domain experts. During the final evaluation, the doctors ranked our system higher than a standard baseline solution and a variation for the used NLP methods.
PerSleep: A Visual Analytics Approach for Performance Assessment of Sleep Staging Models
(The Eurographics Association, 2021) Garcia Caballero, Humberto S.; Corvò, Alberto; Meulen, Fokke van; Fonseca, Pedro; Overeem, Sebasitaan; Wijk, Jarke J. van; Westenberg, Michel A.; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
Machine learning is becoming increasingly popular in the medical domain. In the near future, clinicians expect predictive models to support daily tasks such as diagnosis and prognostic analysis. For this reason, it is utterly important to evaluate and compare the performance of such models so that clinicians can safely rely on them. In this paper, we focus on sleep staging wherein machine learning models can be used to automate or support sleep scoring. Evaluation of these models is complex because sleep is a natural process, which varies among patients. For adoption in clinical routine, it is important to understand how the models perform for different groups of patients. Moreover, models can be trained to recognize different characteristics in the data, and model developers need to understand why and how performance of the different models varies. To address these challenges, we present a visual analytics approach to evaluate the performance of predictive models on sleep staging and to help experts better understand these models with respect to patient data (e.g., conditions, medication, etc.). We illustrate the effectiveness of our approach by comparing multiple models trained on real-world sleep staging data with experts.
The Role of Depth Perception in XR from a Neuroscience Perspective: A Primer and Survey
(The Eurographics Association, 2021) Hushagen, Vetle; Tresselt, Gustav C.; Smit, Noeska N.; Specht, Karsten; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
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.
Shading Style Assessment for Vessel Wall and Lumen Visualization
(The Eurographics Association, 2021) Ostendorf, Kai; Mastrodicasa, Domenico; Bäumler, Kathrin; Codari, Marina; Turner, Valery; Willemink, Martin J.; Fleischmann, Dominik; Preim, Bernhard; Mistelbauer, Gabriel; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
Current blood vessel rendering usually depicts solely the surface of vascular structures and does not visualize any interior structures. While this approach is suitable for most applications, certain cardiovascular diseases, such as aortic dissection would benefit from a more comprehensive visualization. In this work, we investigate different shading styles for the visualization of the aortic inner and outer wall, including the dissection flap. Finding suitable shading algorithms, techniques, and appropriate parameters is time-consuming when practitioners fine-tune them manually. Therefore, we build a shading pipeline using wellknown shading algorithms such as Blinn-Phong, Oren-Nayar, Cook-Torrance, Toon, and extended Lit-Sphere shading with techniques such as the Fresnel effect and screen space ambient occlusion. We interviewed six experts from various domains to find the best combination of shadings for preset combinations that maximize user experience and the applicability in clinical settings.
Visual Assessment of Growth Prediction in Brain Structures after Pediatric Radiotherapy
(The Eurographics Association, 2021) Magg, Caroline; Toussaint, Laura; Muren, Ludvig P.; Indelicato, Danny J.; Raidou, Renata Georgia; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
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.
Vologram: An Educational Holographic Sculpture for Volumetric Medical Data Physicalization
(The Eurographics Association, 2021) Pahr, Daniel; Wu, Hsiang-Yun; Raidou, Renata Georgia; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
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.

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