3/27/2023 0 Comments Wolftech gradient![]() We solve these occlusion problems by dynamic cutaway surfaces. As all presented information is spatially intertwined, occlusion problems occur. Our combination of blood flow visualization and wall thickness representation is a significant improvement for the exploration and analysis of aneurysms. Ongoing research emphasizes the importance of analyzing the wall thickness in risk assessment. Therefore, to get a fully informed decision it is essential to both investigate the vessel morphology and the hemodynamic data. Such aneurysms bear a high risk of rupture and significant treatment-related risks. We thus offer medical researchers an effective visual analysis tool for aneurysm treatment risk assessment. Our method uses illustrative techniques to provide occlusion-free visualization of the flow. We present the first visualization tool that combines pathlines from blood flow and wall thickness information. In addition, qualitative feedback shows that PointCast and TraceCast were strongly favored by our participants for intuitiveness and efficiency. Our results show that Cast family members are virtually always faster than existing methods without tradeoffs in accuracy. In addition to presenting Cast, we report on a formal user study that compares our new techniques not only to each other but also to existing state-of-the-art selection methods. Together, they provide an effective and efficient tool set for the fast exploratory analysis of large datasets. They are able to infer a user's subtle selection intention from gestural input, can deal with complex situations such as partially occluded point clusters or multiple cluster layers, and can all be fine-tuned after the selection interaction has been completed. Our new techniques SpaceCast, TraceCast, and PointCast improve usability and speed of spatial selection in point clouds through novel context-aware algorithms. Traditionally, such interactive target selection has been particularly challenging when the data subsets of interest were implicitly defined in the form of complicated structures of thousands of particles. For these datasets, spatial selection is an essential prerequisite to many other analysis tasks. We present a family of three interactive Context-Aware Selection Techniques (CAST) for the analysis of large 3D particle datasets. As a result, users have the ability to interact with medical volume images much like they would do with physical anatomy, directly with their hands. All components of the interaction metaphor have been designed to capture the user’s intent in an intuitive manner, solving the mapping from the 2D touchscreen to the visible elements of the 3D volume. This interaction metaphor relies on novel technical methods that address three major challenges: selection of anatomical elements in volumetric images, mapping of 2D manipulation gestures to 3D transformations, and real-time deformation of the volumetric images. Beyond simple visual inspection, we empower users to reach the visible anatomical elements directly with their hands, and then move and deform them through natural gestures, while respecting the mechanical behavior of the underlying anatomy. In this work, we propose a novel metaphor to interact with volumetric anatomical images, e.g., magnetic resonance imaging or computed tomography scans. In our experiments we show that adaptions of existing approaches to monocular depth estimation perform well on semi-transparent volume renderings, which has several applications in the area of scientific visualization. This layered representation consists of spatially separated semi-transparent intervals that composite to the original input rendering. Additionally, we investigate how these networks can be extended to further obtain color and opacity information, in order to create a layered representation of the scene based on a single color image. As depth is notoriously difficult to define in a volumetric scene without clearly defined surfaces, we consider different depth computations that have emerged in practice, and compare state-of-the-art monocular depth estimation approaches for these different interpretations during an evaluation considering different degrees of opacity in the renderings. In this work we investigate the applicability of such monocular depth estimation networks to semi-transparent volume rendered images. Especially, monocular depth estimation networks are increasingly reliable in real-world scenes. Neural networks have shown great success in extracting geometric information from color images. ![]()
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