• Visual Analytics for the Integrated Exploration and Sensemaking of Cancer Cohort Radiogenomics and Clinical Information 

  El-Sherbiny, Sarah; Ning, Jing; Hantusch, Brigitte; Kenner, Lukas; Raidou, Renata Georgia (The Eurographics Association, 2023)

  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 ...

• Cytosplore Simian Viewer: Visual Exploration for Multi-Species Single-Cell RNA Sequencing Data 

  Basu, Soumyadeep; Eggermont, Jeroen; Kroes, Thomas; Jorstad, Nikolas; Bakken, Trygve; Lein, Ed; Lelieveldt, Boudewijn; Höllt, Thomas (The Eurographics Association, 2023)

  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 ...

• Rapid Prototyping for Coordinated Views of Multi-scale Spatial and Abstract Data: A Grammar-based Approach 

  Harth, Philipp; Bast, Arco; Troidl, Jakob; Meulemeester, Bjorge; Pfister, Hanspeter; Beyer, Johanna; Oberlaender, Marcel; Hege, Hans-Christian; Baum, Daniel (The Eurographics Association, 2023)

  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 ...

• Smoke Surfaces of 4D Biological Dynamical Systems 

  Schindler, Marwin; Amirkhanov, Aleksandr; Raidou, Renata Georgia (The Eurographics Association, 2023)

  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 ...

• Reflections on AI-Assisted Character Design for Data-Driven Medical Stories 

  Budich, Beatrice; Garrison, Laura A.; Preim, Bernhard; Meuschke, Monique (The Eurographics Association, 2023)

  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 ...

• Communicating Pathologies and Growth to a General Audience 

  Mittenentzwei, Sarah; Mlitzke, Sophie; Lawonn, Kai; Preim, Bernhard; Meuschke, Monique (The Eurographics Association, 2023)

  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 ...

• Bio-Sketch: A New Medium for Interactive Storytelling Illustrated by the Phenomenon of Infection 

  Olivier, Pauline; Chabrier, Renaud; Memari, Pooran; Coll, Jean-Luc; Cani, Marie-Paule (The Eurographics Association, 2023)

  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 ...

• CDF-Based Importance Sampling and Visualization for Neural Network Training 

  Knutsson, Alex; Unnebäck, Jakob; Jönsson, Daniel; Eilertsen, Gabriel (The Eurographics Association, 2023)

  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 ...

• An Interaction Metaphor for Enhanced VR-based Volume Segmentation 

  Monclús, Eva; Vázquez, Pere-Pau (The Eurographics Association, 2023)

  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 ...

• NeRF for 3D Reconstruction from X-ray Angiography: Possibilities and Limitations 

  Maas, Kirsten W. H.; Pezzotti, Nicola; Vermeer, Amy J. E.; Ruijters, Danny; Vilanova, Anna (The Eurographics Association, 2023)

  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 ...

• Neural Deformable Cone Beam CT 

  Birklein, Lukas; Schömer, Elmar; Brylka, Robert; Schwanecke, Ulrich; Schulze, Ralf (The Eurographics Association, 2023)

  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 ...

• Resectograms: Real-Time 2D Visualization of Liver Virtual Resections 

  Meng, Ruoyan; Aghayan, Davit; Pelanis, Egidijus; Edwin, Bjørn; Cheikh, Faouzi Alaya; Palomar, Rafael (The Eurographics Association, 2023)

  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 ...

• Interactive Visual Exploration of Region-based Sensitivities in Fiber Tracking 

  Siddiqui, Faizan; Höllt, Thomas; Vilanova, Anna (The Eurographics Association, 2023)

  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 ...

• Visual Analytics to Support Treatment Decisions in Late-Stage Melanoma Patients 

  Pereira, Calida; Niemann, Uli; Braun, Andreas; Mengoni, Miriam; Tüting, Thomas; Preim, Bernhard; Meuschke, Monique (The Eurographics Association, 2023)

  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 ...

• Eurographics Workshop on Visual Computing for Biology and Medicine: Frontmatter 

  Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas (The Eurographics Association, 2023)

• A Stratification Matrix Viewer for Analysis of Neural Network Data 

  Harth, Philipp; Vohra, Sumit; Udvary, Daniel; Oberlaender, Marcel; Hege, Hans-Christian; Baum, Daniel (The Eurographics Association, 2022)

  The analysis of brain networks is central to neurobiological research. In this context the following tasks often arise: (1) understand the cellular composition of a reconstructed neural tissue volume to determine the nodes ...

• Predicting, Analyzing and Communicating Outcomes of COVID-19 Hospitalizations with Medical Images and Clinical Data 

  Stritzel, Oliver; Raidou, Renata Georgia (The Eurographics Association, 2022)

  We propose PACO, 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 ...

• Understanding Graph Convolutional Networks to detect Brain Lesions from Stroke 

  Iporre-Rivas, Ariel; Scheuermann, Gerik; Gillmann, Christina (The Eurographics Association, 2022)

  Brain lesions derived from stroke episodes can result in disabilities for a patient. Therefore, the segmentation of brain lesions is an important task in neurology. Recently this task has been mainly tackled by machine ...

• Visual Analytics to Assess Deep Learning Models for Cross-Modal Brain Tumor Segmentation 

  Magg, Caroline; Raidou, Renata Georgia (The Eurographics Association, 2022)

  Accurate delineations of anatomically relevant structures are required for cancer treatment planning. Despite its accuracy, manual labeling is time-consuming and tedious-hence, the potential of automatic approaches, such ...

• HistoContours: a Framework for Visual Annotation of Histopathology Whole Slide Images 

  Al-Thelaya, Khaled; Joad, Faaiz; Gilal, Nauman Ullah; Mifsud, William; Pintore, Giovanni; Gobbetti, Enrico; Agus, Marco; Schneider, Jens (The Eurographics Association, 2022)

  We present an end-to-end framework for histopathological analysis of whole slide images (WSIs). Our framework uses deep learning-based localization & classification of cell nuclei followed by spatial data aggregation to ...

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