Preoperative MR imaging in endometrial cancer patients provides valuable information on local tumor extent, which routinely guides choice of surgical procedure and adjuvant therapy. Furthermore, ...whole-volume tumor analyses of MR images may provide radiomic tumor signatures potentially relevant for better individualization and optimization of treatment. We apply a convolutional neural network for automatic tumor segmentation in endometrial cancer patients, enabling automated extraction of tumor texture parameters and tumor volume. The network was trained, validated and tested on a cohort of 139 endometrial cancer patients based on preoperative pelvic imaging. The algorithm was able to retrieve tumor volumes comparable to human expert level (likelihood-ratio test, Formula: see text). The network was also able to provide a set of segmentation masks with human agreement not different from inter-rater agreement of human experts (Wilcoxon signed rank test, Formula: see text, Formula: see text, and Formula: see text). An automatic tool for tumor segmentation in endometrial cancer patients enables automated extraction of tumor volume and whole-volume tumor texture features. This approach represents a promising method for automatic radiomic tumor profiling with potential relevance for better prognostication and individualization of therapeutic strategy in endometrial cancer.
In this article, we present a method for rendering dynamic scenes featuring translucent procedural volumetric detail with all-frequency soft shadows being cast from objects residing inside the view ...frustum. Our approach is based on an approximation of physically correct shadows from distant Gaussian area light sources positioned behind the view plane, using iterative convolution. We present a theoretical and empirical analysis of this model and propose an efficient class of convolution kernels which provide high quality at interactive frame rates. Our GPU-based implementation supports arbitrary volumetric detail maps, requires no precomputation, and therefore allows for real-time modification of all rendering parameters.
A large variety of severe medical conditions involve alterations in microvascular circulation. Hence, measurements or simulation of circulation and perfusion has considerable clinical value and can ...be used for diagnostics, evaluation of treatment efficacy, and for surgical planning. However, the accuracy of traditional tracer kinetic one-compartment models is limited due to scale dependency. As a remedy, we propose a scale invariant mathematical framework for simulating whole brain perfusion. The suggested framework is based on a segmentation of anatomical geometry down to imaging voxel resolution. Large vessels in the arterial and venous network are identified from time-of-flight (ToF) and quantitative susceptibility mapping (QSM). Macro-scale flow in the large-vessel-network is accurately modelled using the Hagen-Poiseuille equation, whereas capillary flow is treated as two-compartment porous media flow. Macro-scale flow is coupled with micro-scale flow by a spatially distributing support function in the terminal endings. Perfusion is defined as the transition of fluid from the arterial to the venous compartment. We demonstrate a whole brain simulation of tracer propagation on a realistic geometric model of the human brain, where the model comprises distinct areas of grey and white matter, as well as large vessels in the arterial and venous vascular network. Our proposed framework is an accurate and viable alternative to traditional compartment models, with high relevance for simulation of brain perfusion and also for restoration of field parameters in clinical brain perfusion applications.
In this paper, we present a novel technique which simulates directional light scattering for more realistic interactive visualization of volume data. Our method extends the recent directional ...occlusion shading model by enabling light source positioning with practically no performance penalty. Light transport is approximated using a tilted cone‐shaped function which leaves elliptic footprints in the opacity buffer during slice‐based volume rendering. We perform an incremental blurring operation on the opacity buffer for each slice in front‐to‐back order. This buffer is then used to define the degree of occlusion for the subsequent slice. Our method is capable of generating high‐quality soft shadowing effects, allows interactive modification of all illumination and rendering parameters, and requires no pre‐computation.
Neurobiology investigates how anatomical and physiological relationships in the nervous system mediate behavior. Molecular genetic techniques, applied to species such as the common fruit fly ...Drosophila melanogaster, have proven to be an important tool in this research. Large databases of transgenic specimens are being built and need to be analyzed to establish models of neural information processing. In this paper we present an approach for the exploration and analysis of neural circuits based on such a database. We have designed and implemented \emph{BrainGazer}, a system which integrates visualization techniques for volume data acquired through confocal microscopy as well as annotated anatomical structures with an intuitive approach for accessing the available information. We focus on the ability to visually query the data based on semantic as well as spatial relationships. Additionally, we present visualization techniques for the concurrent depiction of neurobiological volume data and geometric objects which aim to reduce visual clutter. The described system is the result of an ongoing interdisciplinary collaboration between neurobiologists and visualization researchers.
We provide an automatic algorithm proceeding from raw automatic identification system (AIS) data, through filters, highlighting vessel types, cargo, or regions, to discrete transport patterns, ...describing the geographic, directional, and total volume of transport. The designed algorithm automatically detects major ports or zones, where vessels are stationary and close to land, and groups them hierarchically in order to determine the end nodes in a transport matrix. In conjunction with vessel type, size, and draught information, the transport matrix is then used to compile transported tonnage and vessel count, to display as trade flows between nodes. In addition, by clicking on individual routes, the system also provides quantitative details, such as summed tonnage, or passing vessel counts. We use trades carried out by VLCC and Supramax bulk carriers as examples to demonstrate the results. The contribution of the research is threefold. First, we use AIS data to aggregate “real-time” trade flows. This substantially reduces latency from the traditional way of mapping flows and generating trade statistics. Second, the ability to visualize changes in seasonal or regional trading patterns enables economists and policy makers to monitor changes at a macro level. Finally, the ability to dripple down to the individual ship level allows commercial traders to monitor vessels or company performances, which provides valuable information in trading and decision-making.
A Perceptual-Statistics Shading Model Solteszova, V.; Turkay, C.; Price, M. C. ...
IEEE transactions on visualization and computer graphics,
12/2012, Letnik:
18, Številka:
12
Journal Article
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The process of surface perception is complex and based on several influencing factors, e.g., shading, silhouettes, occluding contours, and top down cognition. The accuracy of surface perception can ...be measured and the influencing factors can be modified in order to decrease the error in perception. This paper presents a novel concept of how a perceptual evaluation of a visualization technique can contribute to its redesign with the aim of improving the match between the distal and the proximal stimulus. During analysis of data from previous perceptual studies, we observed that the slant of 3D surfaces visualized on 2D screens is systematically underestimated. The visible trends in the error allowed us to create a statistical model of the perceived surface slant. Based on this statistical model we obtained from user experiments, we derived a new shading model that uses adjusted surface normals and aims to reduce the error in slant perception. The result is a shape-enhancement of visualization which is driven by an experimentally-founded statistical model. To assess the efficiency of the statistical shading model, we repeated the evaluation experiment and confirmed that the error in perception was decreased. Results of both user experiments are publicly-available datasets.
The importance of a seaport depends on how well it is connected in a transportation network. A port's connectivity is therefore one of the key issues in determining its competitiveness and ...developments in regions and countries. We construct a port connectivity index for major Norwegian ports based on a unique dataset derived from the automated identification system (AIS) for multiple vessel types over a 7-year period. Port connectivity is evaluated empirically by the number of unique vessel visits, vessel sizes, and cargo sizes. The research has implications for port authorities and policy makers in the areas of port planning, infrastructure investment, short sea shipping promotion, and environmental policies. The contributions of this research are twofold. First, the methodology linking the AIS vessel-tracking system with port connectivity is a pioneering empirical application of maritime big data. Second, the port connectivity index is constructed for multiple vessel types and regional port groups, which is an improvement from the current literature where conceptual measures are constructed based on hypothetical and usually too simple optimization rules. The methodology can be easily expanded to other regions in the world.
Current visualization technology implemented in the software for 2D sonars used in marine research is limited to slicing whilst volume visualization is only possible as post processing. We designed ...and implemented a system which allows for instantaneous volume visualization of streamed scans from 2D sonars without prior resampling to a voxel grid. The volume is formed by a set of most recent scans which are being stored. We transform each scan using its associated transformations to the view-space and slice their bounding box by view-aligned planes. Each slicing plane is reconstructed from the underlying scans and directly used for slice-based volume rendering. We integrated a low frequency illumination model which enhances the depth perception of noisy acoustic measurements. While we visualize the 2D data and time as 3D volumes, the temporal dimension is not intuitively communicated. Therefore, we introduce a concept of temporal outlines. Our system is a result of an interdisciplinary collaboration between visualization and marine scientists. The application of our system was evaluated by independent domain experts who were not involved in the design process in order to determine real life applicability.
Real‐time volume data acquisition poses substantial challenges for the traditional visualization pipeline where data enhancement is typically seen as a pre‐processing step. In the case of 4D ...ultrasound data, for instance, costly processing operations to reduce noise and to remove artefacts need to be executed for every frame. To enable the use of high‐quality filtering operations in such scenarios, we propose an output‐sensitive approach to the visualization of streaming volume data. Our method evaluates the potential contribution of all voxels to the final image, allowing us to skip expensive processing operations that have little or no effect on the visualization. As filtering operations modify the data values which may affect the visibility, our main contribution is a fast scheme to predict their maximum effect on the final image. Our approach prioritizes filtering of voxels with high contribution to the final visualization based on a maximal permissible error per pixel. With zero permissible error, the optimized filtering will yield a result that is identical to filtering of the entire volume. We provide a thorough technical evaluation of the approach and demonstrate it on several typical scenarios that require on‐the‐fly processing.
Real‐time volume data acquisition poses substantial challenges for the traditional visualization pipeline where data enhancement is typically seen as a pre‐processing step. In the case of 4D ultrasound data, for instance, costly processing operations to reduce noise and to remove artefacts need to be executed for every frame. To enable the use of high‐quality filtering operations in such scenarios, we propose an outputsensitive approach to the visualization of streaming volume data. Our method evaluates the potential contribution of all voxels to the final image, allowing us to skip expensive processing operations that have little or no effect on the visualization As filtering operations modify the data values which may affect the visibility, our main contribution is a fast scheme to predict their maximum effect on the final image. Our approach prioritizes filtering of voxels with high contribution to the final visualization based on a maximal permissible error per pixel. With zero permissible error, the optimized filtering will yield a result that is identical to filtering of the entire volume. We provide a thorough technical evaluation of the approach and demonstrate it on several typical scenarios that require on‐the‐fly processing.
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