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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Bio-Sketch: A New Medium for Interactive Storytelling Illustrated by the Phenomenon of Infection
(The Eurographics Association, 2023) Olivier, Pauline; Chabrier, Renaud; Memari, Pooran; Coll, Jean-Luc; Cani, Marie-Paule; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
In the field of biology, digital illustrations play a crucial role in conveying complex phenomena, allowing for idealized shapes and motion, in contrast to data visualization. In the absence of suitable media, scientists often rely on oversimplified 2D figures or have to call in professional artists to create better illustrations, which can be limiting. We introduce Bio-Sketch, a novel progressive sketching system designed to ease the creation of animated illustrations, as exemplified here in the context of the infection phenomenon. Our solution relies on a new progressive sketching paradigm that seamlessly combines 3D modeling and pattern-based shape distribution to create background volume and temporal animation control. The elements created can be assembled into a complex scenario, enabling narrative design experiments for educational applications in biology. Our results and first feedback from experts in illustration and biology demonstrate the potential of Bio-Sketch to assist communication on the infection phenomenon, helping to bridge the gap between expert and non-expert audiences.
CDF-Based Importance Sampling and Visualization for Neural Network Training
(The Eurographics Association, 2023) Knutsson, Alex; Unnebäck, Jakob; Jönsson, Daniel; Eilertsen, Gabriel; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
Training a deep neural network is computationally expensive, but achieving the same network performance with less computation is possible if the training data is carefully chosen. However, selecting input samples during training is challenging as their true importance for the optimization is unknown. Furthermore, evaluation of the importance of individual samples must be computationally efficient and unbiased. In this paper, we present a new input data importance sampling strategy for reducing the training time of deep neural networks. We investigate different importance metrics that can be efficiently retrieved as they are available during training, i.e., the training loss and gradient norm. We found that choosing only samples with large loss or gradient norm, which are hard for the network to learn, is not optimal for the network performance. Instead, we introduce an importance sampling strategy that selects samples based on the cumulative distribution function of the loss and gradient norm, thereby making it more likely to choose hard samples while still including easy ones. The behavior of the proposed strategy is first analyzed on a synthetic dataset, and then evaluated in the application of classification of malignant cancer in digital pathology image patches. As pathology images contain many repetitive patterns, there could be significant gains in focusing on features that contribute stronger to the optimization. Finally, we show how the importance sampling process can be used to gain insights about the input data through visualization of samples that are found most or least useful for the training.
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.
Cytosplore Simian Viewer: Visual Exploration for Multi-Species Single-Cell RNA Sequencing Data
(The Eurographics Association, 2023) Basu, Soumyadeep; Eggermont, Jeroen; Kroes, Thomas; Jorstad, Nikolas; Bakken, Trygve; Lein, Ed; Lelieveldt, Boudewijn; Höllt, Thomas; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
With the rapid advances in single-cell sequencing technologies, novel types of studies into the cell-type makeup of the brain have become possible. Biologists often analyze large and complex single-cell transcriptomic datasets to enhance knowledge of the intricate features of cellular and molecular tissue organization. A particular area of interest is the study of whether cell types and their gene regulation are conserved across species during evolution. However, in-depth comparisons across species of such high-dimensional, multi-modal single-cell data pose considerable visualization challenges. This paper introduces Cytosplore Simian Viewer, a visualization system that combines various views and linked interaction methods for comparative analysis of single-cell transcriptomic datasets across multiple species. Cytosplore Simian Viewer enables biologists to help gain insights into the cell type and gene expression differences and similarities among different species, particularly focusing on comparing human data to other species. The system validation in discovery research on real-world datasets demonstrates its utility in visualizing valuable results related to the evolutionary development of the middle temporal gyrus.
Eurographics Workshop on Visual Computing for Biology and Medicine: Frontmatter
(The Eurographics Association, 2023) Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
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.
Interactive Visual Exploration of Region-based Sensitivities in Fiber Tracking
(The Eurographics Association, 2023) Siddiqui, Faizan; Höllt, Thomas; Vilanova, Anna; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
Fiber tracking is a powerful technique that provides valuable insights into the complex white matter structure of the human brain. However, the processing pipeline involves many sources of uncertainty, with one notable factor being the user-defined parameters that significantly influence the resulting outputs. Among these parameters, the definition of seed-points is a crucial aspect in most fiber tracking algorithms. These seed-points are determined through regions of interest (ROI) and serve as the initial points for fiber tract generation. In this work, we present an interactive technique that utilizes seed-point sensitivities to guide the definition of regions of interest (ROI). We examine various scenarios where sensitivity information can enhance the ROI definition process and provide user guidelines and recommended actions for each scenario. Building upon this analysis, we have developed a visualization strategy that enables users to explore seed-point sensitivities effectively and facilitate the definition of optimal ROIs. We present results highlighting the benefits of the proposed visual design in the clinical pipelines.
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.
Neural Deformable Cone Beam CT
(The Eurographics Association, 2023) Birklein, Lukas; Schömer, Elmar; Brylka, Robert; Schwanecke, Ulrich; Schulze, Ralf; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
In oral and maxillofacial cone beam computed tomography (CBCT), patient motion is frequently observed and, if not accounted for, can severely affect the usability of the acquired images. We propose a highly flexible, data driven motion correction and reconstruction method which combines neural inverse rendering in a CBCT setting with a neural deformation field. We jointly optimize a lightweight coordinate based representation of the 3D volume together with a deformation network. This allows our method to generate high quality results while accurately representing occurring patient movements, such as head movements, separate jaw movements or swallowing. We evaluate our method in synthetic and clinical scenarios and are able to produce artefact-free reconstructions even in the presence of severe motion. While our approach is primarily developed for maxillofacial applications, we do not restrict the deformation field to certain kinds of motion. We demonstrate its flexibility by applying it to other scenarios, such as 4D lung scans or industrial tomography settings, achieving state-of-the art results within minutes with only minimal adjustments.
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.
Reflections on AI-Assisted Character Design for Data-Driven Medical Stories
(The Eurographics Association, 2023) Budich, Beatrice; Garrison, Laura A.; Preim, Bernhard; Meuschke, Monique; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
Data-driven storytelling has experienced significant growth in recent years to become a common practice in various application areas, including healthcare. Within the realm of medical narratives, characters play a pivotal role in connecting audiences with data and conveying complex medical information in an engaging manner that may influence positive behavioral and lifestyle changes on the part of the viewer. However, the process of designing characters that are both informative and engaging remains a challenge. In this paper, we propose an AI-assisted pipeline for character design in the context of data-driven medical stories. Our iterative pipeline blends design sensibilities with automation to reduce the time and artistic expertise needed to develop characters reflective of the underlying data, even when that data is time-oriented as in a cohort study.
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
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].
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.
Visual Analytics to Support Treatment Decisions in Late-Stage Melanoma Patients
(The Eurographics Association, 2023) Pereira, Calida; Niemann, Uli; Braun, Andreas; Mengoni, Miriam; Tüting, Thomas; Preim, Bernhard; Meuschke, Monique; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
We present a visual analytics system to support treatment decisions in late-stage Melanoma patients. With the aim of improving patient outcomes, personalized treatment decisions based on individual characteristics and medical histories are crucial. The research focuses on the design and development of a visual analytics system tailored specifically for tumor boards, where multidisciplinary teams collaborate to make informed decisions. By leveraging a comprehensive database containing treatment and tumor stage progression information from over 1100 patients, the system provides healthcare professionals with a holistic overview and facilitates the analysis of individual cases as well as comparisons between multiple patients. The distinction between tumor board preparation systems and systems used during discussions is emphasized to ensure user-centric design and usability. Through the use of visual analytics techniques, complex relationships between treatment outcomes, temporal features, and patient-specific factors are explored, enabling clinicians to identify patterns and trends that may impact treatment decisions. The findings of this research contribute to the growing field of visual analytics in healthcare and have the potential to enhance treatment decision-making and patient care in late-stage cancer scenarios.

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