Visually comparing brain networks, or connectomes, is an essential task in the field of neuroscience. Especially relevant to the field of clinical neuroscience, group studies that examine differences between populations or changes over time within a population enable neuroscientists to reason about effective diagnoses and treatments for a range of neuropsychiatric disorders. In this paper, we specifically explore how visual analytics tools can be used to facilitate various clinical neuroscience tasks, in which observation and analysis of meaningful patterns in the connectome can support patient diagnosis and treatment. We conduct a survey of visualization tasks that enable clinical neuroscience activities, and further explore how existing connectome visualization tools support or fail to support these tasks. Based on our investigation of these tasks, we introduce a novel visualization tool, NeuroCave, to support group studies analyses. We discuss how our design decisions (the use of immersive visualization, the use of hierarchical clustering and dimensionality reduction techniques, and the choice of visual encodings) are motivated by these tasks. We evaluate NeuroCave through two use cases that illustrate the utility of interactive connectome visualization in clinical neuroscience contexts. In the first use case, we study sex differences using functional connectomes and discover hidden connectome patterns associated with well-known cognitive differences in spatial and verbal abilities. In the second use case, we show how the utility of visualizing the brain in different topological space coupled with clustering information can reveal the brain's intrinsic structure.
While most commonly used visualization tools are dedicated desktop applications, web-based implementations, such as @cite_4 or @cite_15 , free the user from being attached to a specific operating system @cite_52 . is also a web-based application and runs in any modern browser. The use of stereoscopic techniques can provide a more immersive way to explore brain imaging data @cite_9 , H "a find that healthcare professionals perceive the increased dimensionality provided by stereoscopy as beneficial for understanding depth in the displayed scenery @cite_53 . Moreover, Ware and Mitchell find that the use of stereographic visualizations reduces the perception error rate in graph perception for large graphs with more than 500 nodes @cite_6 , and @cite_0 observed that, when coupled with highlighting, stereoscopic representations of 3D graphs outperformed their non-immersive counterpart. lets the user move between desktop and VR environments for interactively exploring 3D connectomes.
@cite_4 @cite_9 @cite_53 @cite_52 @cite_6 @cite_0 @cite_15
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It has been known for some time that larger graphs can be interpreted if laid out in 3D and displayed with stereo and or motion depth cues to support spatial perception. However, prior studies were carried out using displays that provided a level of detail far short of what the human visual system is capable of resolving. Therefore, we undertook a graph comprehension study using a very high resolution stereoscopic display. In our first experiment, we examined the effect of stereoscopic display, kinetic depth, and using 3D tubes versus lines to display the links. The results showed a much greater benefit for 3D viewing than previous studies. For example, with both motion and stereoscopic depth cues, unskilled observers could see paths between nodes in 333 node graphs with less than a 10p error rate. Skilled observers could see up to a 1000-node graph with less than a 10p error rate. This represented an order of magnitude increase over 2D display. In our second experiment, we varied both nodes and links to understand the constraints on the number of links and the size of graph that can be reliably traced. We found the difference between number of links and number of nodes to best account for error rates and suggest that this is evidence for a “perceptual phase transition.” These findings are discussed in terms of their implications for information display. In this paper we present a new technique and prototype graph visualization system, stereoscopic highlighting, to help answer accessibility and adjacency queries when interacting with a node-link diagram. Our technique utilizes stereoscopic depth to highlight regions of interest in a 2D graph by projecting these parts onto a plane closer to the viewpoint of the user. This technique aims to isolate and magnify specific portions of the graph that need to be explored in detail without resorting to other highlighting techniques like color or motion, which can then be reserved to encode other data attributes. This mechanism of stereoscopic highlighting also enables focus+context views by juxtaposing a detailed image of a region of interest with the overall graph, which is visualized at a further depth with correspondingly less detail. In order to validate our technique, we ran a controlled experiment with 16 subjects comparing static visual highlighting to stereoscopic highlighting on 2D and 3D graph layouts for a range of tasks. Our results show that while for most tasks the difference in performance between stereoscopic highlighting alone and static visual highlighting is not statistically significant, users performed better when both highlighting methods were used concurrently. In more complicated tasks, 3D layout with static visual highlighting outperformed 2D layouts with a single highlighting method. However, it did not outperform the 2D layout utilizing both highlighting techniques simultaneously. Based on these results, we conclude that stereoscopic highlighting is a promising technique that can significantly enhance graph visualizations for certain use cases. Effective visualization is central to the exploration and comprehension of brain imaging data. While MRI data are acquired in three-dimensional space, the methods for visualizing such data have rarely taken advantage of three-dimensional stereoscopic technologies. We present here results of stereoscopic visualization of clinical data, as well as an atlas of whole-brain functional connectivity. In comparison with traditional 3D rendering techniques, we demonstrate the utility of stereoscopic visualizations to provide an intuitive description of the exact location and the relative sizes of various brain landmarks, structures and lesions. In the case of resting state fMRI, stereoscopic 3D visualization facilitated comprehension of the anatomical position of complex large-scale functional connectivity patterns. Overall, stereoscopic visualization improves the intuitive visual comprehension of image contents, and brings increased dimensionality to visualization of traditional MRI data, as well as patterns of functional connectivity. We introduce a novel projection-based visualization method for high-dimensional data sets by combining concepts from MDS and the geometry of the hyperbolic spaces. This approach hyperbolic multi-dimensional scaling (H-MDS) is a synthesis of two important concepts for explorative data analysis and visualization: (i) multi-dimensional scaling uses proximity or pair distance data to generate a low-dimensional, spatial presentation of the data; (ii) previous work on the "hyperbolic tree browser" demonstrated the extraordinary advantages for an interactive display of graph-like data in the two-dimensional hyperbolic space (H2).In the new approach, H-MDS maps proximity data directly into the H2. This removes the restriction to "quasihierarchical", graph-based data--a major limitation of (ii). Since a suitable distance function can convert all kinds of data to proximity (or distance-based) data, this type of data can be considered the most general.We review important properties of the hyperbolic space and, in particular, the circular Poincare model of the H2. It enables effective human-computer interaction: by mouse dragging the "focus", the user can navigate in the data without loosing the context. In H2 the "fish-eye" behavior originates not simply by a non-linear view transformation but rather by extraordinary, non-Euclidean properties of the H2. Especially, the exponential growth of length and area of the underlying space makes the H2 a prime target for mapping hierarchical and (now also) high-dimensional data.Several high-dimensional mapping examples including synthetic and real-world data are presented. Since high-dimensional data produce "ring"-shaped displays, we present methods to enhance the display by modulating the dissimilarity contrast. This is demonstrated for an application for unstructured text: i.e., by using multiple film critiques from news:rec.art.movies.reviews and www.imdb.com, each movie is placed within the H2--creating a "space of movies" for interactive exploration. Many real-world domains can be represented as large node-link graphs: backbone Internet routers connect with 70,000 other hosts, mid-sized Web servers handle between 20,000 and 200,000 hyperlinked documents, and dictionaries contain millions of words defined in terms of each other. Computational manipulation of such large graphs is common, but previous tools for graph visualization have been limited to datasets of a few thousand nodes. Visual depictions of graphs and networks are external representations that exploit human visual processing to reduce the cognitive load of many tasks that require understanding of global or local structure. We assert that the two key advantages of computer-based systems for information visualization over traditional paper-based visual exposition are interactivity and scalability. We also argue that designing visualization software by taking the characteristics of a target user's task domain into account leads to systems that are more effective and scale to larger datasets than previous work. This thesis contains a detailed analysis of three specialized systems for the interactive exploration of large graphs, relating the intended tasks to the spatial layout and visual encoding choices. We present two novel algorithms for specialized layout and drawing that use quite different visual metaphors. The H3 system for visualizing the hyperlink structures of web sites scales to datasets of over 100,000 nodes by using a carefully chosen spanning tree as the layout backbone, 3D hyperbolic geometry for a Focus+Context view, and provides a fluid interactive experience through guaranteed frame rate drawing. The Constellation system features a highly specialized 2D layout intended to spatially encode domain-specific information for computational linguists checking the plausibility of a large semantic network created from dictionaries. The Planet Multicast system for displaying the tunnel topology of the Internet's multicast backbone provides a literal 3D geographic layout of arcs on a globe to help MBone maintainers find misconfigured long-distance tunnels. Each of these three systems provides a very different view of the graph structure, and we evaluate their efficacy for the intended task. We generalize these findings in our analysis of the importance of interactivity and specialization for graph visualization systems that are effective and scalable. Stereoscopic 3D (S3D) movies have become widely popular in the movie theaters, but the adoption of S3D at home is low even though most TV sets support S3D. It is widely believed that S3D with glasses is not the right approach for the home. A much more appealing approach is to use automulti-scopic displays that provide a glasses-free 3D experience to multiple viewers. A technical challenge is the lack of native multiview content that is required to deliver a proper view of the scene for every viewpoint. Our approach takes advantage of the abundance of stereoscopic 3D movies. We propose a real-time system that can convert stereoscopic video to a high-quality multiview video that can be directly fed to automultiscopic displays. Our algorithm uses a wavelet-based decomposition of stereoscopic images with per-wavelet disparity estimation. A key to our solution lies in combining Lagrangian and Eulerian approaches for both the disparity estimation and novel view synthesis, which leverages the complementary advantages of both techniques. The solution preserves all the features of Eulerian methods, e.g., subpixel accuracy, high performance, robustness to ambiguous depth cases, and easy integration of inter-view aliasing while maintaining the advantages of Lagrangian approaches, e.g., robustness to large disparities and possibility of performing non-trivial disparity manipulations through both view extrapolation and interpolation. The method achieves real-time performance on current GPUs. Its design also enables an easy hardware implementation that is demonstrated using a field-programmable gate array. We analyze the visual quality and robustness of our technique on a number of synthetic and real-world examples. We also perform a user experiment which demonstrates benefits of the technique when compared to existing solutions. Though the variety of desktop real time stereo vision systems has grown considerably in the past several years, few make any verifiable claims about the accuracy of the algorithms used to construct 3D data or describe how the data generated by such systems, which is large in size, can be effectively distributed. In this paper, we describe a system that creates an accurate (on the order of a centimeter), 3D reconstruction of an environment in real time (under 30 ms) that also allows for remote interaction between users. This paper addresses how to reconstruct, compress, and visualize the 3D environment. In contrast to most commercial desktop real time stereo vision systems our algorithm produces 3D meshes instead of dense point clouds, which we show allows for better quality visualizations. The chosen representation of the data also allows for high compression ratios for transfer to remote sites. We demonstrate the accuracy and speed of our results on a variety of benchmarks. This paper presents the first attempt at stereoscopic neural style transfer, which responds to the emerging demand for 3D movies or AR VR. We start with a careful examination of applying existing monocular style transfer methods to left and right views of stereoscopic images separately. This reveals that the original disparity consistency cannot be well preserved in the final stylization results, which causes 3D fatigue to the viewers. To address this issue, we incorporate a new disparity loss into the widely adopted style loss function by enforcing the bidirectional disparity constraint in non-occluded regions. For a practical real-time solution, we propose the first feed-forward network by jointly training a stylization sub-network and a disparity sub-network, and integrate them in a feature level middle domain. Our disparity sub-network is also the first end-to-end network for simultaneous bidirectional disparity and occlusion mask estimation. Finally, our network is effectively extended to stereoscopic videos, by considering both temporal coherence and disparity consistency. We will show that the proposed method clearly outperforms the baseline algorithms both quantitatively and qualitatively. With the increase in popularity of stereoscopic 3D imagery for film, TV, and interactive entertainment, an urgent need for editing tools to support stereo content creation has become apparent. In this paper we present an end-to-end system for object copy & paste in a stereoscopic setting to address this need. There is no straightforward extension of 2D copy & paste to support the addition of the third dimension as we show in this paper. For stereoscopic copy & paste we need to handle depth, and our core objective is to obtain a convincing 3D viewing experience. As one of the main contributions of our system, we introduce a stereo billboard method for stereoscopic rendering of the copied selection. Our approach preserves the stereo volume and is robust to the inevitable inaccuracies of the depth maps computed from a stereo pair of images. Our system also includes an interactive stereoscopic segmentation tool to achieve high quality object selection. Hence, we focus on intuitive and minimal user interaction, and our editing operations perform within interactive rates to provide immediate feedback. Abstract Can semantic corpora be coupled to dynamical simulations in such a way so as to extract new associations from the data that were hitherto unapparent? We attempt to do this within neuroscience as an application domain, by introducing the notion of the semantome and coupling it to the connectome of the human brain network. This is implemented using BrainX3, a virtual reality simulation cum data mining platform that can be used for visualization, analysis and feature extraction of neuroscience data. We use this system to explore anatomical, functional and symptomatic semantics associated to simulated neuronal activity of a healthy brain, one with stroke and one perturbed by transcranial magnetic stimulation. In particular, we find that pariet al and occipital lesions in stroke affect the visual processing pathway leading to symptoms such as visual neglect, depression and photo-sensitivity seizures. Integrating semantomics with connectomics thus generates hypotheses about symptoms, functions and brain activity that supplement existing tools for diagnosis of mental illness. Our results suggest a new approach to big data with potential applications to other domains. Visualization is a critical component of neuroimaging, and how to best view data that is naturally three dimensional is a long standing question in neuroscience. Many approaches, programs, and techniques have been developed specifically for neuroimaging. However, exploration of 3D information through a 2D screen is inherently limited. Many neuroscientific researchers hope that with the recent commercialization and popularization of VR, it can offer the next-step in data visualization and exploration. Neuro Imaging in Virtual Reality (NIVR), is a visualization suite that employs various immersive visualizations to represent neuroimaging information in VR. Some established techniques, such as raymarching volume visualization, are paired with newer techniques, such as near-field rendering, to provide a broad basis of how we can leverage VR to improve visualization and navigation of neuroimaging data. Several of the neuroscientific visualization approaches presented are, to our knowledge, the first of their kind. NIVR offers not only an exploration of neuroscientific data visualization, but also a tool to expose and educate the public regarding recent advancements in the field of neuroimaging. By providing an engaging experience to explore new techniques and discoveries in neuroimaging, we hope to spark scientific interest through a broad audience. Furthermore, neuroimaging offers deep and expansive datasets; a single scan can involve several gigabytes of information. Visualization and exploration of this type of information can be challenging, and real-time exploration of this information in VR even more so. NIVR explores pathways which make this possible, and offers preliminary stereo visualizations of these types of massive data. Cortical network architecture has predominantly been investigated visually using graph theory representations. In the context of human connectomics, such representations are not however always satisfactory because canonical methods for vertex–edge relationship representation do not always offer optimal insight regarding functional and structural neural connectivity. This article introduces an innovative framework for the depiction of human connectomics by employing a circular visualization method which is highly suitable to the exploration of central nervous system architecture. This type of representation, which we name a ‘connectogram’, has the capability of classifying neuroconnectivity relationships intuitively and elegantly. A multimodal protocol for MRI DTI neuroimaging data acquisition is here combined with automatic image segmentation to (1) extract cortical and non-cortical anatomical structures, (2) calculate associated volumetrics and morphometrics, and (3) determine patient-specific connectivity profiles to generate subject-level and population-level connectograms. The scalability of our approach is demonstrated for a population of 50 adults. Two essential advantages of the connectogram are (1) the enormous potential for mapping and analyzing the human connectome, and (2) the unconstrained ability to expand and extend this analysis framework to the investigation of clinical populations and animal models. We present a dense reconstruction approach that overcomes the drawbacks of traditional multiview stereo by incorporating semantic information in the form of learned category-level shape priors and object detection. Given training data comprised of 3D scans and images of objects from various viewpoints, we learn a prior comprised of a mean shape and a set of weighted anchor points. The former captures the commonality of shapes across the category, while the latter encodes similarities between instances in the form of appearance and spatial consistency. We propose robust algorithms to match anchor points across instances that enable learning a mean shape for the category, even with large shape variations across instances. We model the shape of an object instance as a warped version of the category mean, along with instance-specific details. Given multiple images of an unseen instance, we collate information from 2D object detectors to align the structure from motion point cloud with the mean shape, which is subsequently warped and refined to approach the actual shape. Extensive experiments demonstrate that our model is general enough to learn semantic priors for different object categories, yet powerful enough to reconstruct individual shapes with large variations. Qualitative and quantitative evaluations show that our framework can produce more accurate reconstructions than alternative state-of-the-art multiview stereo systems. We propose a novel projection based visualization method for high-dimensional datasets by combining concepts from MDS and the geometry of the hyperbolic spaces. Our approach Hyperbolic Multi-Dimensional Scaling (H-MDS) extends earlier work [7] using hyperbolic spaces for visualization of tree structures data ( "hyperbolic tree browser" ).By borrowing concepts from multi-dimensional scaling we map proximity data directly into the 2-dimensional hyperbolic space (H2). This removes the restriction to "quasihierarchical", graph-based data -- limiting previous work. Since a suitable distance function can convert all kinds of data to proximity (or distance-based) data this type of data can be considered the most general.We used the circular Poincare model of the H2 which allows effective human-computer interaction: by moving the "focus" via mouse the user can navigate in the data without loosing the "context". In H2 the "fish-eye" behavior originates not simply by a non-linear view transformation but rather by extraordinary, non-Euclidean properties of the H2. Especially, the exponential growth of length and area of the underlying space makes the H2 a prime target for mapping hierarchical and (now also) high-dimensional data.We present several high-dimensional mapping examples including synthetic and real world data and a successful application for unstructured text. By analyzing and integrating multiple film critiques from news:rec.art.movies.reviews and the internet movie database, each movie becomes placed within the H2. Here the idea is, that related films share more words in their reviews than unrelated. Their semantic proximity leads to a closer arrangement. The result is a kind of high-level content structured display allowing the user to explore the "space of movies". The quest for deeper understanding of biological systems has driven the acquisition of increasingly larger multidimensional image datasets. Inspecting and manipulating data of this complexity is very challenging in traditional visualization systems. We developed syGlass, a software package capable of visualizing large scale volumetric data with inexpensive virtual reality head-mounted display technology. This allows leveraging stereoscopic vision to significantly improve perception of complex 3D structures, and provides immersive interaction with data directly in 3D. We accomplished this by developing highly optimized data flow and volume rendering pipelines, tested on datasets up to 16TB in size, as well as tools available in a virtual reality GUI to support advanced data exploration, annotation, and cataloguing. Reconstructing a synaptic wiring diagram, or connectome, from electron microscopy (EM) images of brain tissue currently requires many hours of manual annotation or proofreading (Kasthuri and Lichtman, 2010; Lichtman and Sanes, 2008; Seung, 2009). The desire to reconstruct ever larger and more complex networks has pushed the collection of ever larger EM datasets. A cubic millimeter of raw imaging data would take up 1 PB of storage and present an annotation project that would be impractical without relying heavily on automatic segmentation methods. The RhoanaNet image processing pipeline was developed to automatically segment large volumes of EM data and ease the burden of manual proofreading and annotation. Based on (, 2015), we updated every stage of the software pipeline to provide better throughput performance and higher quality segmentation results. We used state of the art deep learning techniques to generate improved membrane probability maps, and Gala (Nunez-, 2014) was used to agglomerate 2D segments into 3D objects. We applied the RhoanaNet pipeline to four densely annotated EM datasets, two from mouse cortex, one from cerebellum and one from mouse lateral geniculate nucleus (LGN). All training and test data is made available for benchmark comparisons. The best segmentation results obtained gave @math scores of 0.9054 and 09182 for the cortex datasets, 0.9438 for LGN, and 0.9150 for Cerebellum. The RhoanaNet pipeline is open source software. All source code, training data, test data, and annotations for all four benchmark datasets are available at this http URL. Given the ease that humans have with using a keyboard and mouse in typical, non-colocated computer interaction, many studies have investigated the value of colocating the visual field and motor workspaces using immersive display modalities. Significant understanding has been gained by previous work comparing physical tasks against virtual tasks, visuomotor colocation versus non-colocation, and even visuomotor rotational misalignments in virtual environments (VEs). However, few studies have explored all of these paradigms in context with each other, and it is difficult to perform interstudy comparisons because of the variation in tested motor tasks. Therefore, using a stereoscopic fish tank display setup, the goal for the current study was to characterize human performance of a 3D Fitts' point-to-point reaching task using a stylus-based haptic interface in the physical, colocated non-colocated, and rotated VE visualization conditions. Five performance measures---throughput, efficiency, initial movement error, corrective movements, and peak velocity---were measured and used to evaluate task performance. These measures were studied in 22 subjects (11 male, 11 female, ages 20--32) performing a 3D variant of Fitts' serial task under 10 task conditions: physical, colocated VE, non-colocated VE, and rotated VEs from 45---315° in 45° increments. Hypotheses: All performance measures in the colocated VE were expected to reflect significantly reduced task performance over the real condition, but also reflect increased performance over the non-colocated VE condition. For rotational misalignments, all performance measures were expected to reflect the highest performance at 0°, reduce to the lowest performance at 90°, and rise again to a local maximum at 180° (symmetric about 0°). Results: All performance measures showed that the colocated VE condition resulted in significantly lower task performance than the physical condition and higher mean performance than the non-colocated VE condition, but the difference was not statistically significant. Also, rotation misalignments showed that task performance was mostly reduced to a minimum at 90°, 135°, and 225°. We conclude that colocated VEs may not significantly improve point-to-point reaching performance over non-colocated VEs. Also, visual rotations of ±45° affected throughput, efficiency, peak velocity, and initial movement error, but the number of corrective movements were not affected until ± 90°. Many modern 3D reconstruction methods accumulate information volumetrically using truncated signed distance functions. While this usually imposes a regular grid with fixed voxel size, not all parts of a scene necessarily need to be represented at the same level of detail. For example, a flat table needs less detail than a highly structured keyboard on it. We introduce a novel representation for the volumetric 3D data that uses hash functions rather than trees for accessing individual blocks of the scene, but which still provides different resolution levels. We show that our data structure provides efficient access and manipulation functions that can be very well parallelised, and also describe an automatic way of choosing appropriate resolutions for different parts of the scene. We embed the novel representation in a system for simultaneous localization and mapping from RGB-D imagery and also investigate the implications of the irregular grid on interpolation routines. Finally, we evaluate our system in experiments, demonstrating state-of-the-art representation accuracy at typical frame-rates around 100 Hz, along with 40 memory savings. We propose a probabilistic formulation of joint silhouette extraction and 3D reconstruction given a series of calibrated 2D images. Instead of segmenting each image separately in order to construct a 3D surface consistent with the estimated silhouettes, we compute the most probable 3D shape that gives rise to the observed color information. The probabilistic framework, based on Bayesian inference, enables robust 3D reconstruction by optimally taking into account the contribution of all views. We solve the arising maximum a posteriori shape inference in a globally optimal manner by convex relaxation techniques in a spatially continuous representation. For an interactively provided user input in the form of scribbles specifying foreground and background regions, we build corresponding color distributions as multivariate Gaussians and find a volume occupancy that best fits to this data in a variational sense. Compared to classical methods for silhouette-based multiview reconstruction, the proposed approach does not depend on initialization and enjoys significant resilience to violations of the model assumptions due to background clutter, specular reflections, and camera sensor perturbations. In experiments on several real-world data sets, we show that exploiting a silhouette coherency criterion in a multiview setting allows for dramatic improvements of silhouette quality over independent 2D segmentations without any significant increase of computational efforts. This results in more accurate visual hull estimation, needed by a multitude of image-based modeling approaches. We made use of recent advances in parallel computing with a GPU implementation of the proposed method generating reconstructions on volume grids of more than 20 million voxels in up to 4.41 seconds. The computation of stereoscopic depth is an important field of computer vision. Although a large variety of algorithms has been developed, the traditional correlation-based versions of these algorithms are prevalent. This is mainly due to easy implementation and handling but also to the linear computational complexity, as compared to more elaborated algorithms based on diffusion processes, graph-cut or bilateral filtering. In this paper, we introduce a new two-stage matching cost for the traditional approach: the summed normalized cross-correlation (SNCC). This new cost function performs a normalized cross-correlation in the first stage and aggregates the correlation values in a second stage. We show that this new measure can be implemented efficiently and that it leads to a substantial improvement of the performance of the traditional stereo approach because it is less sensitive to high contrast outliers.