VCBM 19: Eurographics Workshop on Visual Computing for Biology and Medicine

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https://diglib.eg.org/handle/10.2312/2632802

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Browsing VCBM 19: Eurographics Workshop on Visual Computing for Biology and Medicine by Subject "Computing methodologies"

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    InkVis: A High-Particle-Count Approach for Visualization of Phase-Contrast Magnetic Resonance Imaging Data
    (The Eurographics Association, 2019) de Hoon, Niels; Lawonn, Kai; Jalba, Andrei; Eisemann, Elmar; Vilanova, Anna; Kozlíková, Barbora and Linsen, Lars and Vázquez, Pere-Pau and Lawonn, Kai and Raidou, Renata Georgia
    Phase-Contrast Magnetic Resonance Imaging (PC-MRI) measures volumetric and time-varying blood flow data, unsurpassed in quality and completeness. Such blood-flow data have been shown to have the potential to improve both diagnosis and risk assessment of cardiovascular diseases (CVDs) uniquely. Typically PC-MRI data is visualized using stream- or pathlines. However, time-varying aspects of the data, e.g., vortex shedding, breakdown, and formation, are not sufficiently captured by these visualization techniques. Experimental flow visualization techniques introduce a visible medium, like smoke or dye, to visualize flow aspects including time-varying aspects. We propose a framework that mimics such experimental techniques by using a high number of particles. The framework offers great flexibility which allows for various visualization approaches. These include common traditional flow visualizations, but also streak visualizations to show the temporal aspects, and uncertainty visualizations. Moreover, these patient-specific measurements suffer from noise artifacts and a coarse resolution, causing uncertainty. Traditional flow visualizations neglect uncertainty and, therefore, may give a false sense of certainty, which can mislead the user yielding incorrect decisions. Previously, the domain experts had no means to visualize the effect of the uncertainty in the data. Our framework has been adopted by domain experts to visualize the vortices present in the sinuses of the aorta root showing the potential of the framework. Furthermore, an evaluation among domain experts indicated that having the option to visualize the uncertainty contributed to their confidence on the analysis.
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    Interactive CPU-based Ray Tracing of Solvent Excluded Surfaces
    (The Eurographics Association, 2019) Rau, Tobias; Zahn, Sebastian; Krone, Michael; Reina, Guido; Ertl, Thomas; Kozlíková, Barbora and Linsen, Lars and Vázquez, Pere-Pau and Lawonn, Kai and Raidou, Renata Georgia
    Depictions of molecular surfaces such as the Solvent Excluded Surface (SES) can provide crucial insight into functional molecular properties, such as the molecule's potential to react. The interactive visualization of single and multiple molecule surfaces is essential for the data analysis by domain experts. Nowadays, the SES can be rendered at high frame rates using shader-based ray casting on the GPU. However, rendering large molecules or larger molecule complexes requires large amounts of memory that has the potential to exceed the memory limitations of current hardware. Here we show that rendering using CPU ray tracing also reaches interactive frame rates without hard limitations to memory. In our results large molecule complexes can be rendered with only the precomputation of each individual SES, and no further involved representation or transformation. Additionally, we provide advanced visualization techniques like ambient occlusion opacity mapping (AOOM) to enhance the comprehensibility of the molecular structure. CPU ray tracing not only provides very high image quality and global illumination, which is beneficial for the perception of spatial structures, it also opens up the possibility to visualize larger data sets and to render on any HPC cluster. Our results demonstrate that simple instancing of geometry keeps the memory consumption for rendering large molecule complexes low, so the examination of much larger data is also possible.
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    Layer-Aware iOCT Volume Rendering for Retinal Surgery
    (The Eurographics Association, 2019) Weiss, Jakob; Eck, Ulrich; Nasseri, Muhamad Ali; Maier, Mathias; Eslami, Abouzar; Navab, Nassir; Kozlíková, Barbora and Linsen, Lars and Vázquez, Pere-Pau and Lawonn, Kai and Raidou, Renata Georgia
    Retinal microsurgery is one of the most challenging types of surgery, yet in practice, intraoperative digital assistance is rare. The introduction of fast, microscope integrated Optical Coherence Tomography (iOCT) has enabled intraoperative imaging of subsurface structures. However, effective intraoperative visualization of this data poses a challenging problem both in terms of performance and engineering as well as for creating easily interpretable visualizations of this data. Most existing research focuses on visualization of diagnostic OCT data where imaging quality is higher and processing times are not an issue. We introduce a perceptually linear color map for a separated encoding of tissue reflectivity and positional information as chrominance and luminance. Based on this color mapping, we propose a Direct Volume Rendering (DVR) method that aids structure perception. To aid subretinal injection tasks, we introduce a novel Layer-Adjusted Maximum Intensity Projection (LA-MIP), correcting for the natural curvature of the retinal tissue. Expert feedback suggests our methods are preferred over baseline methods. Further research is needed to confirm the benefits of our approach in routine clinical applications.
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    Multiparametric Magnetic Resonance Image Synthesis using Generative Adversarial Networks
    (The Eurographics Association, 2019) Haarburger, Christoph; Horst, Nicolas; Truhn, Daniel; Broeckmann, Mirjam; Schrading, Simone; Kuhl, Christiane; Merhof, Dorit; Kozlíková, Barbora and Linsen, Lars and Vázquez, Pere-Pau and Lawonn, Kai and Raidou, Renata Georgia
    Generative adversarial networks have been shown to alleviate the problem of limited training data for supervised learning problems in medical image computing. However, most generative models for medical images focus on image-to-image translation rather than de novo image synthesis. In many clinical applications, image acquisition is multiparametric, i.e. includes contrast-enchanced or diffusion-weighted imaging. We present a generative adversarial network that synthesizes a sequence of temporally consistent contrast-enhanced breast MR image patches. Performance is evaluated quantitatively using the Fréchet Inception Distance, achieving a minimum FID of 21.03. Moreover, a qualitative human reader test shows that even a radiologist cannot differentiate between real and fake images easily.