VCBM 2023: Eurographics Workshop on Visual Computing for Biology and Medicine

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https://diglib.eg.org/handle/10.2312/3543877

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Browsing VCBM 2023: Eurographics Workshop on Visual Computing for Biology and Medicine by Subject "Applied computing"

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    Communicating Pathologies and Growth to a General Audience
    (The Eurographics Association, 2023) Mittenentzwei, Sarah; Mlitzke, Sophie; Lawonn, Kai; Preim, Bernhard; Meuschke, Monique; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
    In this paper, we investigate the suitability of different visual representations of pathological growth using surface models of intracranial aneurysms and liver tumors. By presenting complex medical information in a visually accessible manner, audiences can better understand and comprehend the progression of pathological structures. Previous work in medical visualization provides an extensive design space for visualizing medical image data. However, determining which visualization techniques are appropriate for a general audience has not been thoroughly investigated. We conducted a user study (n = 60) to evaluate different visual representations in terms of their suitability for solving tasks and their aesthetics. We created surface models representing the evolution of pathological structures over multiple discrete time steps and visualized them using illumination-based and illustrative techniques. Our results indicate that the suitability of visualization techniques depends on the task at hand. Users' aesthetic preferences largely coincide with their preferred visualization technique for task-solving purposes.
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    An Interaction Metaphor for Enhanced VR-based Volume Segmentation
    (The Eurographics Association, 2023) Monclús, Eva; Vázquez, Pere-Pau; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
    The segmentation of medical models is a complex and time-intensive process required for both diagnosis and surgical preparation. Despite the advancements in deep learning, neural networks can only automatically segment a limited number of structures, often requiring further validation by a domain expert. In numerous instances, manual segmentation is still necessary. Virtual Reality (VR) technology can enhance the segmentation process by providing improved perception of segmentation outcomes and enabling interactive supervision by experts. But inspecting how the progress of the segmentation algorithm is evolving, and defining new seeds requires seeing the inner layers of the volume, which can be costly and difficult to achieve with typical metaphors such as clipping planes. In this paper, we introduce a wedge-shaped 3D interaction metaphor designed to facilitate VR-based segmentation through detailed inspection and guidance. User evaluations demonstrated increased satisfaction with usability and faster task completion times using the tool.
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    NeRF for 3D Reconstruction from X-ray Angiography: Possibilities and Limitations
    (The Eurographics Association, 2023) Maas, Kirsten W. H.; Pezzotti, Nicola; Vermeer, Amy J. E.; Ruijters, Danny; Vilanova, Anna; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
    Neural Radiance Field (NeRF) is a promising deep learning technique based on neural rendering for three-dimensional (3D) reconstruction. This technique has overcome several limitations of 3D reconstruction techniques, such as removing the need for 3D ground truth or two-dimensional (2D) segmentations. In the medical context, the 3D reconstruction of vessels from 2D X-ray angiography is a relevant problem. For example, the treatment of coronary arteries could still benefit from 3D reconstruction solutions, as common solutions do not suffice. Challenging areas in the 3D reconstruction from X-ray angiography are the vessel morphology characteristics, such as sparsity, overlap, and the distinction between foreground and background. Moreover, sparse view and limited angle X-ray projections restrict the information available for the 3D reconstructions. Many traditional and machine learning methods have been proposed, but they rely on demanding user interactions or require large amounts of training data. NeRF could solve these limitations, given that promising results have been shown for medical (X-ray) applications. However, to the best of our knowledge, no results have been shown with X-ray angiography projections or consider the vessel morphology characteristics. This paper explores the possibilities and limitations of using NeRF for 3D reconstruction from X-ray angiography. An extensive experimental analysis is conducted to quantitatively and qualitatively evaluate the effects of the X-ray angiographic challenges on the reconstruction quality. We demonstrate that NeRF has the potential for 3D Xray angiography reconstruction (e.g., reconstruction with sparse and limited angle X-ray projections) but also identify explicit limitations (e.g., the overlap of background structures) that must be addressed in future works.
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    Rapid Prototyping for Coordinated Views of Multi-scale Spatial and Abstract Data: A Grammar-based Approach
    (The Eurographics Association, 2023) Harth, Philipp; Bast, Arco; Troidl, Jakob; Meulemeester, Bjorge; Pfister, Hanspeter; Beyer, Johanna; Oberlaender, Marcel; Hege, Hans-Christian; Baum, Daniel; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
    Visualization grammars are gaining popularity as they allow visualization specialists and experienced users to quickly create static and interactive views. Existing grammars, however, mostly focus on abstract views, ignoring three-dimensional (3D) views, which are very important in fields such as natural sciences. We propose a generalized interaction grammar for the problem of coordinating heterogeneous view types, such as standard charts (e.g., based on Vega-Lite) and 3D anatomical views. An important aspect of our web-based framework is that user interactions with data items at various levels of detail can be systematically integrated and used to control the overall layout of the application workspace. With the help of a concise JSON-based specification of the intended workflow, we can handle complex interactive visual analysis scenarios. This enables rapid prototyping and iterative refinement of the visual analysis tool in collaboration with domain experts. We illustrate the usefulness of our framework in two real-world case studies from the field of neuroscience. Since the logic of the presented grammar-based approach for handling interactions between heterogeneous web-based views is free of any application specifics, it can also serve as a template for applications beyond biological research.
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    Resectograms: Real-Time 2D Visualization of Liver Virtual Resections
    (The Eurographics Association, 2023) Meng, Ruoyan; Aghayan, Davit; Pelanis, Egidijus; Edwin, Bjørn; Cheikh, Faouzi Alaya; Palomar, Rafael; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
    Visualization of virtual resections plays a central role in computer-assisted liver surgery planning. The complexity of the liver's internal structures often leads to difficulties in its proper visualization during the positioning of virtual resections. Occlusions by vessels and tumors are common problems leading to non-preservation of resection margin, incorrect intersection with vessels, and resections. To overcome these challenges, we introduce Resectograms: a visualization approach based on 2D representations of virtual resections, which enable the visualization of information associated with surgical planning. These representations are presented as an additional 2D view displaying anatomical, functional, and risk-associated information extracted from the virtual resection in real-time. This view offers surgeons a simple and occlusion-free visualization of the virtual resection during surgical planning. Our pilot experiment with clinicians shows that the use of this visualization tool provides more information while planning virtual resections and has the potential to enhance confidence in accurate resection. The code repository and supplementary materials for this work is available at: https://github.com/ALive-research/Slicer-Liver
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    Smoke Surfaces of 4D Biological Dynamical Systems
    (The Eurographics Association, 2023) Schindler, Marwin; Amirkhanov, Aleksandr; Raidou, Renata Georgia; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
    To study biological phenomena, mathematical biologists often employ modeling with ordinary differential equations. A system of ordinary differential equations that describes the state of a phenomenon as a moving point in space across time is known as a dynamical system. This moving point emerges from the initial condition of the system and is referred to as a trajectory that ''lives'' in phase space, i.e., a space that defines all possible states of the system. In our previous work, we proposed Many- Lands [AKS*19]-an approach to explore and analyze typical trajectories of 4D dynamical systems, using smooth, animated transitions to navigate through phase space. However, in ManyLands the comparison of multiple trajectories emerging from different initial conditions does not scale well, due to overdrawing that clutters the view. We extend ManyLands to support the comparative visualization of multiple trajectories of a 4D dynamical system, making use of smoke surfaces. In this way, the sensitivity of the dynamical system to its initialization can be investigated. The 4D smoke surfaces can be further projected onto lower-dimensional subspaces (3D and 2D) with seamless animated transitions. We showcase the capabilities of our approach using two 4D dynamical systems from biology [Gol11, KJS06] and a 4D dynamical system exhibiting chaotic behavior [Bou15].
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    Visual Analytics for the Integrated Exploration and Sensemaking of Cancer Cohort Radiogenomics and Clinical Information
    (The Eurographics Association, 2023) El-Sherbiny, Sarah; Ning, Jing; Hantusch, Brigitte; Kenner, Lukas; Raidou, Renata Georgia; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
    We present a visual analytics (VA) framework for the comprehensive exploration and integrated analysis of radiogenomic and clinical data from a cancer cohort. Our framework aims to support the workflow of cancer experts and biomedical data scientists as they investigate cancer mechanisms. Challenges in the analysis of radiogenomic data, such as the heterogeneity and complexity of the data sets, hinder the exploration and sensemaking of the available patient information. These challenges can be answered through the field of VA, but approaches that bridge radiogenomic and clinical data in an interactive and flexible visual framework are still lacking. Our approach enables the integrated exploration and joint analysis of radiogenomic data and clinical information for knowledge discovery and hypothesis assessment through a flexible VA dashboard. We follow a user-centered design strategy, where we integrate domain knowledge into a semi-automated analytical workflow based on unsupervised machine learning to identify patterns in the patient data provided by our collaborating domain experts. An interactive visual interface further supports the exploratory and analytical process in a free and a hypothesis-driven manner. We evaluate the unsupervised machine learning models through similarity measures and assess the usability of the framework through use cases conducted with cancer experts. Expert feedback indicates that our framework provides suitable and flexible means for gaining insights into large and heterogeneous cancer cohort data, while also being easily extensible to other data sets.