VCBM 12: Eurographics Workshop on Visual Computing for Biology and Medicine

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/464

Browse

Now showing 1 - 17 of 17

Atomistic Visualization of Mesoscopic Whole-Cell Simulations
(The Eurographics Association, 2012) Falk, Martin; Krone, Michael; Ertl, Thomas; Timo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdink
Molecular visualizations are a principal tool for analyzing the results of biochemical simulations. With modern GPU ray casting approaches it is only possible to render several millions of atoms at interactive frame rates unless advanced acceleration methods are employed. But even simplified cell models of whole-cell simulations consist of at least several billion atoms. However, many instances of only a few different proteins occur in the intracellular environment, which is beneficial in order to fit the data into the graphics memory. One model is stored for each protein species and rendered once per instance. The proposed method exploits recent algorithmic advances for particle rendering and the repetitive nature of intracellular proteins to visualize dynamic results from mesoscopic simulations of cellular transport processes. We present two out-of-core optimizations for the interactive visualization of data sets composed of billions of atoms as well as details on the data preparation and the employed rendering techniques. Furthermore, we apply advanced shading methods to improve the image quality including methods to enhance depth and shape perception besides non-photorealistic rendering methods.
BrainCove: A Tool for Voxel-wise fMRI Brain Connectivity Visualization
(The Eurographics Association, 2012) Dixhoorn, André F. van; Milles, Julien R.; Lew, Baldur van; Botha, Charl P.; Timo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdink
Functional brain connectivity from fMRI studies has become an important tool in studying functional interactions in the human brain as a complex network. Most recently, research has started focusing on whole brain functional networks at the voxel-level, where fMRI time-signals at each voxel are correlated with every other voxel in the brain to determine their functional connectivity. For a typical 4mm isotropic voxel resolution, this results in connectivity networks with more than twenty thousand nodes and over 400 million links. These cannot be effectively visualized or interactively explored using node-link representations, and due to their size are challenging to show as correlation matrix bitmaps. In this paper, we present a number of methods for the visualization and interactive visual analysis of this new high resolution brain network data, both in its matrix representation as well as in its anatomical context. We have implemented these methods in a GPU raycasting framework that enables real-time interaction, such as network probing and volume deformation, as well as real-time filtering. The techniques are integrated in a visual analysis application in which the different views are coupled, supporting linked interaction. Furthermore, we allow visual comparison of different brain networks with side-by-side and difference visualization. We have evaluated our approach via case studies with domain scientists at two different university medical centers.
The BundleExplorer: A Focus and Context Rendering Framework for Complex Fiber Distributions
(The Eurographics Association, 2012) Röttger, Diana; Merhof, Dorit; Müller, Stefan; Timo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdink
Advanced diffusion imaging enables the reconstruction of complex fiber configurations such as crossings or fannings. However, resulting visualizations often suffer from visual clutter which makes exploration challenging. This paper presents the BundleExplorer as a GPU-based focus and context rendering framework for diffusion data. A combination of a fiber encompassing hull and line rendering is proposed to provide insight into inner-bundle fiber configurations as well as to enable bundle crossing exploration. Visual clutter is reduced and information about the global bundle geometry is provided by using fiber encompassing hulls. At the same time important characteristics, such as individual trajectory courses, which are conventionally neglected when using hull visualizations, are revealed by cutaway techniques and enhanced line renderings. In addition, spatial features, the distance to the fiber hull, as well as functional features, i.e., the degree of anisotropy, are visualized using fiber color encoding. Different cutaway techniques using marker and view-dependent clippings are implemented in order to reveal focus information. Visual enhancements are used to indicate bundle intersections.
Combining B-Mode and Color Flow Vessel Segmentation for Registration of Hepatic CT and Ultrasound Volumes
(The Eurographics Association, 2012) Keil, Matthias; Laura, Cristina Oyarzun; Drechsler, Klaus; Wesarg, Stefan; Timo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdink
Multimodal registration of intraoperative ultrasound and preoperative computed tomography imaging is the basis for percutaneous hepatic interventions. Currently, a rigid registration is performed manually by the surgeon using vessel structures and other anatomical landmarks for visual guidance. In this work our approach for intraoperative vessel segmentation from two ultrasound imaging modes, namely B-Mode and color flow mode, is presented. This segmentation is an important step for automation of the intraoperative registration which relies on vessel structures visible in contrast enhanced CT and ultrasound volumes. This paper describes the problems that arise when using B-mode ultrasound for segmentation of vessels and how they can be solved by introducing additional vessel information from color flow imaging. On a total number of 21 patients, our system was applied successfully in 15 cases. For nine randomly chosen patients studied in this paper, our system achieves a 3.45 mm accuracy at points used for registration and 5.01 mm for other landmarks which were not used for the registration process.
Constrained Labeling of 2D Slice Data for Reading Images in Radiology
(The Eurographics Association, 2012) Mogalle, Katja; Tietjen, Christian; Soza, Grzegorz; Preim, Bernhard; Timo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdink
An important and underestimated task to support reading of images in radiology is a proper annotation of findings. In radiology reading, 2D slice images from a given modality (e.g. CT or MRI) need to be analyzed carefully by a radiologist, whereas all clinical relevant findings have to be annotated in the images. This includes information in particular for documentation, follow-up investigations and medical team meetings. The main problem of the automatic placement of labels in a clinical context is to find an arrangement of multiple variable-sized labels which guarantees readability, clearness and unambiguity and avoids occlusion of the image itself. Based on a case study of abdominal CT-Images in an oncologic context we analyze the main constraints for label placement in order to extract candidate label positions, evaluate these and determine valid and good label positions. Based on this preprocessing step, different approaches can be applied for placing multiple labels in a scene. We present a new method called Shifting and compare it to other labeling strategies.
Deriving and Visualizing Uncertainty in Kinetic PET Modeling
(The Eurographics Association, 2012) Nguyen, Khoa Tan; Bock, Alexander; Ynnerman, Anders; Ropinski, Timo; Timo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdink
Kinetic modeling is the tool of choice when developing new positron emission tomography (PET) tracers for quantitative functional analysis. Several approaches are widely used to facilitate this process. While all these approaches are inherently different, they are still subject to uncertainty arising from various stages of the modeling process. In this paper we propose a novel approach for deriving and visualizing uncertainty in kinetic PET modeling. We distinguish between intra- and inter-model uncertainties. While intra-model uncertainty allows us to derive uncertainty based on a single modeling approach, inter-model uncertainty arises from the differences of the results of different approaches. To derive intra-model uncertainty we exploit the covariance matrix analysis. The inter-model uncertainty is derived by comparing the outcome of three standard kinetic PET modeling approaches. We derive and visualize this uncertainty to exploit it as a basis for changing model input parameters with the ultimate goal to reduce the modeling uncertainty and thus obtain a more realistic model of the tracer under investigation. To support this uncertainty reduction process, we visually link abstract and spatial data by introducing a novel visualization approach based on the ThemeRiver metaphor, which has been modified to support the uncertainty-aware visualization of parameter changes between spatial locations. We have investigated the benefits of the presented concepts by conducting an evaluation with domain experts.
From Imprecise User Input to Precise Vessel Segmentations
(The Eurographics Association, 2012) Diepenbrock, Stefan; Ropinski, Timo; Timo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdink
Vessel segmentation is an important prerequisite for many medical applications. While automatic vessel segmentation is an active field of research, interaction and visualization techniques for semi-automatic solutions have gotten far less attention. Nevertheless, since automatic techniques do not generally achieve perfect results, interaction is necessary. Especially for tasks that require an in-detail inspection or analysis of the shape of vascular structures precise segmentations are essential. However, in many cases these can only be generated by incorporating expert knowledge. In this paper we propose a visual vessel segmentation system that allows the user to interactively generate vessel segmentations. Therefore, we employ multiple linked views which allow to assess different aspects of the segmentation and depict its different quality metrics. Based on these quality metrics, the user is guided, can assess the segmentation quality in detail and modify the segmentation accordingly. One common modification is the editing of branches, for which we propose a semi-automatic sketch-based interaction metaphor. Additionally, the user can also influence the shape of the vessel wall or the centerline through sketching. To assess the value of our system we discuss feedback from medical experts and have performed a thorough evaluation.
Geometrical-Acoustics-based Ultrasound Image Simulation
(The Eurographics Association, 2012) Law, Yuen C.; Knott, Thomas; Hentschel, Bernd; Kuhlen, Torsten; Timo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdink
Brightness modulation (B-Mode) ultrasound (US) images are used to visualize internal body structures during diagnostic and invasive procedures, such as needle insertion for Regional Anesthesia. Due to patient availability and health risks-during invasive procedures-training is often limited, thus, medical training simulators become a viable solution to the problem. Simulation of ultrasound images for medical training requires not only an acceptable level of realism but also interactive rendering times in order to be effective. To address these challenges, we present a generative method for simulating B-Mode ultrasound images using surface representations of the body structures and geometrical acoustics to model sound propagation and its interaction within soft tissue. Furthermore, physical models for backscattered, reflected and transmitted energies as well as for the beam profile are used in order to improve realism. Through the proposed methodology we are able to simulate, in real-time, plausible view- and depth-dependent visual artifacts that are characteristic in B-Mode US images, achieving both, realism and interactivity.
Impact of Physical Noise Modeling on Image Segmentation in Echocardiography
(The Eurographics Association, 2012) Tenbrinck, Daniel; Sawatzky, Alex; Jiang, Xiaoyi; Burger, Martin; Haffner, Wladimir; Willems, Patrick; Paul, Matthias; Stypmann, Jörg; Timo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdink
Segmentation is an essential task in ultrasound image analysis. Recently, the trend in literature is towards incorporation of high-level information, e.g., shape priors, since many low-level segmentation techniques suffer from the characteristics of medical ultrasound images, i.e., speckle noise, scattering artifacts, and shadowing effects. However, the majority of these works implicitly assume an additive Gaussian noise model in ultrasound images, although a strong deviation from this assumption is well known, and the impact of correct physical noise modeling is not examined sufficiently until now. In this paper we investigate the influence of three different noise models from literature using a variational region-based segmentation framework, which allows for the incorporation of both low-level and high-level information. We demonstrate that correct physical noise modeling is of high importance for the computation of accurate segmentation results. The numerical results are validated on real patient datasets from echocardiographic examinations and compared to manual segmentations from echocardiographic experts.
Interactive Residual Stress Modeling for Soft Tissue Simulation
(The Eurographics Association, 2012) Wu, Jun; Bürger, Kai; Westermann, Rüdiger; Dick, Christian; Timo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdink
Residual stress is the stress which remains in a deformable body in the absence of external forces. Due to the release of residual stress after cutting, soft tissues will shrink and the wound will open. Thus, to realistically simulate soft tissue deformations due to cutting, a model for the residual stress in a patient body is needed. In this paper we present an interactive method to compute a physically meaningful patient-specific residual stress distribution. With our method, by using their experience doctors can sketch directional stress strokes and specify stress magnitudes at a few control points on the body surface. The residual stress is then immediately computed from these inputs and visualized by displaying the deformations of a set of control cuts on the body. In a visually guided session, the user can further modify the initial strokes and magnitudes until a satisfactory result is obtained. We demonstrate the potential of the proposed method for virtual cut simulation by showing the variations of wound openings depending on the residual stress distribution.
Lowest-Variance Streamlines for Filtering of 3D Ultrasound
(The Eurographics Association, 2012) Soltészová, Veronika; Helljesen, Linn Emilie Sævil; Wein, Wolfgang; Gilja, Odd Helge; Viola, Ivan; Timo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdink
Ultrasound as an acoustic imaging modality suffers from various kinds of noise. The presence of noise especially hinders the 3D visualization of ultrasound data, both in terms of resolving the spatial occlusion of the signal by surrounding noise, and mental decoupling of the signal from noise. This paper presents a novel type of structurepreserving filter that has been specifically designed to eliminate the presence of speckle and random noise in 3D ultrasound datasets. This filter is based on a local distribution of variance for a given voxel. The lowest variance direction is assumed to be aligned with the direction of the structure. A streamline integration over the lowest-variance vector field defines the filtered output value. The new filter is compared to other popular filtering approaches and its superiority is documented on several use cases. A case study where a clinician was delineating vascular structures of the liver from 3D visualizations further demonstrates the benefits of our approach compared to the state of the art.
On the Value of Multi-Volume Visualization for Preoperative Planning of Cerebral AVM Surgery
(The Eurographics Association, 2012) Weiler, Florian; Rieder, Christian; David, Carlos A.; Wald, Christoph; Hahn, Horst K.; Timo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdink
Surgical treatment of cerebral arteriovenous malformations (AVMs) requires thourough preoperative planning for the intervening neurosurgeon. The goal of such planning is to gain a precise understanding of the patho-anatomy of the malformation, specifically about the location and spatial relation of normal and abnormal structures. A key element in this process is the identication and localization of arteries feeding into the lesion, and veins draining it. In this paper, we demonstrate how state-of-the-art techniques from the field of computer graphics and image processing can support neurosurgeons with this task. We address the problem by merging multiple angiographic image sets within a 3D volume rendering pipeline. Datasets from clinical imaging studies were remotely processed at our institute, returned to the institution of origin, and finally visualized in an interactive application, allowing the neurosurgeon to explore the different images simultaneously. Here, we present three case studies along with the medical assessment of an experienced neurosurgeon.
Sketch-based Image-independent Editing of 3D Tumor Segmentations using Variational Interpolation
(The Eurographics Association, 2012) Heckel, Frank; Braunewell, Stefan; Soza, Grzegorz; Tietjen, Christian; Hahn, Horst K.; Timo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdink
In the past years sophisticated automatic segmentation algorithms for various medical image segmentation problems have been developed. However, there are always cases where automatic algorithms fail to provide an acceptable segmentation. In these cases the user needs efficient segmentation correction tools, a problem which has not received much attention in research. Cases to be manually corrected are often particularly difficult and the image does often not provide enough information for segmentation, so we present an image-independent method for intuitive sketch-based editing of 3D tumor segmentations. It is based on an object reconstruction using variational interpolation and can be used in any 3D modality, such as CT or MRI. We also discuss sketch-based editing in 2D as well as a hole-correction approach for variational interpolation. Our manual correction algorithm has been evaluated on 89 segmentations of tumors in CT by 2 technical experts with 6+ years of experience in tumor segmentation and assessment. The experts rated the quality of our correction tool as acceptable or better in 92.1% of the cases. They needed a median number of 4 correction steps with one step taking 0.4s on average.
Synaptic Connectivity in Anatomically Realistic Neural Networks: Modeling and Visual Analysis
(The Eurographics Association, 2012) Dercksen, Vincent J.; Egger, Robert; Hege, Hans-Christian; Oberlaender, Marcel; Timo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdink
The structural organization of neural circuitry is an important determinant of brain function. Thus, knowing the brain's wiring (the connectome) is key to understanding how it works. For example, understanding how sensory information is translated into behavior requires a comprehensive view of the microcircuits performing this translation at the level of individual neurons and synapses. Obtaining a wiring diagram, however, is nontrivial due to size, complexity and accessibility of the involved brain regions. Even when such data were available, it were difficult to analyze. Here we describe how a network of around 0.5 million neurons and their synaptic connections, representing the vibrissal area of the rat primary somatosensory cortex, can be reconstructed. Furthermore, we present a framework for visual exploration of synaptic connectivity between (groups of) neurons within this model. It includes, first, the Cortical Column Connectivity Viewer (CCCV) that provides a hybrid abstract/spatial representation of the connections between neurons of different cell types and/or in different cortical columns. Second, it comprises a 3D view of cell type-specific synapse positions on selected morphologies. This framework is thus an effective tool to visually explore structural organization principles at the population, individual neuron and synapse levels.
Tractography in Context: Multimodal Visualization of Probabilistic Tractograms in Anatomical Context
(The Eurographics Association, 2012) Berres, Anne; Goldau, Mathias; Tittgemeyer, Marc; Scheuermann, Gerik; Hagen, Hans; Timo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdink
Multi-modal display of neurological data in anatomical context is a challenging issue in biomedical visualization. We present an application-driven approach, which solves the visibility issues arising from the simultaneous presentation of probabilistic tractograms and anatomical context. The tractogram (a scalar field indicating a connectivity score between voxels) is visualized by nested surface layers, providing an overview of long-range connectivity. Unique dataset features are reflected by value-based opacity and further enhanced by depth cues. An illustrative, three-dimensional rendering of the cortex complemented with textured slices is provided as anatomical context. The presented methods are based on a detailed requirements analysis with domain experts. Two user studies were performed to evaluate our methods and the techniques were improved based on their feedback. Our methods can be applied to a wide range of data, as they can be adapted to the range and requirements of data very easily.
Visualization and Exploration of 3D Toponome Data
(The Eurographics Association, 2012) Oeltze, Steffen; Klemm, Paul; Hillert, Reyk; Preim, Bernhard; Schubert, Walter; Timo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdink
The toponome of a cell describes the location and topological distribution of proteins across the cell. In toponomics, the toponome is imaged and its inner structure and its semantics are investigated in order to understand how cells encode different functionalities both in health and disease. Toponome imaging results in complex multiparameter data composed of a 3D volume per protein affinity reagent. After imaging, the data is binarized such that 1 encodes protein present and 0 encodes protein absent. Biologists are particularly interested in the clustering of these binary protein patterns and in the distribution of clusters across the cell. We present a volume rendering approach for visualizing all unique protein patterns in 3D. A unique color is dynamically assigned to each pattern such that a sufficient perceptual difference between colors in the current view is guaranteed. We further present techniques for interacting with the view in an exploratory analysis. The biologist may for instance ''peel of'' clusters thereby revealing occluded cell structures. The 3D view is integrated in a multiple coordinated view system. Peeling off clusters or brushing protein patterns in the view updates all other views. We demonstrate the utility of the view with a cell sample containing lymphocytes.
Visually Guided Mesh Smoothing for Medical Applications
(The Eurographics Association, 2012) Moench, Tobias; Kubisch, Christoph; Lawonn, Kai; Westermann, Ruediger; Preim, Bernhard; Timo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdink
Surface models derived from medical image data often exhibit artifacts, such as noise and staircases, which can be reduced by applying mesh smoothing filters. Usually, an iterative adaption of smoothing parameters to the specific data and continuous re-evaluation of accuracy and curvature is required. Depending on the number of vertices and the filter algorithm, computation time may vary strongly and interfere with an interactive mesh generation procedure. In this paper, we present an approach to improve the handling of mesh smoothing filters. Based on a GPU mesh smoothing implementation, model quality is evaluated in real-time and provided to the user as quality graphs to support the mental optimization of input parameters. Moreover, this framework is used to find optimal smoothing parameters automatically and to provide data-specific parameter suggestions.

Browse

Browsing VCBM 12: Eurographics Workshop on Visual Computing for Biology and Medicine by Title

Results Per Page

Sort Options