Visual Motion Analysis For Robotic Tracking Tasks

In the field of computational vision, \u27motion understanding\u27 roughly describes a system\u27s ability to extract information about the 3D position, trajectory, or structure of visible objects by analysing the way their 2D images change over time. Although this inverse problem is ill-posed at outset, it is possible to utilize the principle of spatio-temporal coherence the hypothesis that objects surfaces and motion are locally continuous--to form localized estimates of the changing state of regions of the image. Two main results are achieved in the thesis:;A technique is presented for estimating the infinitesimal translation group component acting at a local spatial neighbourhood of a visual signal. A stochastic estimation technique is developed, and tested using Monte Carlo methods. Its development addresses the problems that arise due to the fact that the estimates of translation must be derived from local neighbourhoods. In other words, their measures must have controllable support over finite spatial domains. According to the same constraints on measurement which give rise to the Heisenberg uncertainty principle, this spatial localization imposes a finite uncertainty on the observations of translation. The developed framework provides a measure of this uncertainty.;Using this method for estimating the action of the translation group, the framework is extended to span the six degrees-of-freedom of the 3D Euclidean motion group. In the 2D image, this group action is modelled locally by the six-parameter tangent space of the 2D affine group. Since not all of the elements of this group commute, a controlled decomposition of their individual actions is needed in order to estimate the changing states resulting from 3D motions. The decomposition is specified by the Lie algebra of the 2D affine group, and implicates a need for tracking and data-directed estimation for computational motion perception

Editorial: Challenges for the usability of AR and VR for clinical neurosurgical procedures

There are a number of challenges that must be faced when trying to develop AR and VR-based Neurosurgical simulators, Surgical Navigation Platforms, and “Smart OR” systems. Trying to simulate an operating room environment and surgical tasks in Augmented and Virtual Reality is a challenge many are attempting to solve, in order to train surgeons or help them operate. What are some of the needs of the surgeon, and what are the challenges encountered (human computer interface, perception, workflow, etc). We discuss these tradeoffs and conclude with critical remarks

Design and evaluation of an augmented reality simulator using leap motion

Advances in virtual and augmented reality (AR) are having an impact on the medical field in areas such as surgical simulation. Improvements to surgical simulation will provide students and residents with additional training and evaluation methods. This is particularly important for procedures such as the endoscopic third ventriculostomy (ETV), which residents perform regularly. Simulators such as NeuroTouch, have been designed to aid in training associated with this procedure. The authors have designed an affordable and easily accessible ETV simulator, and compare it with the existing NeuroTouch for its usability and training effectiveness. This simulator was developed using Unity, Vuforia and the leap motion (LM) for an AR environment. The participants, 16 novices and two expert neurosurgeons, were asked to complete 40 targeting tasks. Participants used the NeuroTouch tool or a virtual hand controlled by the LM to select the position and orientation for these tasks. The length of time to complete each task was recorded and the trajectory log files were used to calculate performance. The resulting data from the novices\u27 and experts\u27 speed and accuracy are compared, and they discuss the objective performance of training in terms of the speed and accuracy of targeting accuracy for each system

Real-time Interactive Tractography Analysis for Multimodal Brain Visualization Tool: MultiXplore

Most debilitating neurological disorders can have anatomical origins. Yet unlike other body organs, the anatomy alone cannot easily provide an understanding of brain functionality. In fact, addressing the challenge of linking structural and functional connectivity remains in the frontiers of neuroscience. Aggregating multimodal neuroimaging datasets may be critical for developing theories that span brain functionality, global neuroanatomy and internal microstructures. Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) are main such techniques that are employed to investigate the brain under normal and pathological conditions. FMRI records blood oxygenation level of the grey matter (GM), whereas DTI is able to reveal the underlying structure of the white matter (WM). Brain global activity is assumed to be an integration of GM functional hubs and WM neural pathways that serve to connect them. In this study we developed and evaluated a two-phase algorithm. This algorithm is employed in a 3D interactive connectivity visualization framework and helps to accelerate clustering of virtual neural pathways. In this paper, we will detail an algorithm that makes use of an index-based membership array formed for a whole brain tractography file and corresponding parcellated brain atlas. Next, we demonstrate efficiency of the algorithm by measuring required times for extracting a variety of fiber clusters, which are chosen in such a way to resemble all sizes probable output data files that algorithm will generate. The proposed algorithm facilitates real-time visual inspection of neuroimaging data to further the discovery in structure-function relationship of the brain networks

Multimodal connectivity based eloquence score computation and visualisation for computer-aided neurosurgical path planning

Non-invasive assessment of cognitive importance has been a major challenge for planning of neurosurgical procedures. In the past decade, in vivo brain imaging modalities have been considered for estimating the \u27eloquence\u27 of brain areas. In order to estimate the impact of damage caused by an access path towards a target region inside of the skull, multi-modal metrics are introduced in this paper. Accordingly, this estimated damage is obtained by combining multi-modal metrics. In other words, this damage is an aggregate of intervened grey matter volume and axonal fibre numbers, weighted by their importance within the assigned anatomical and functional networks. To validate these metrics, an exhaustive search algorithm is implemented for characterising the solution space and visually representing connectional cost associated with a path initiated from underlying points. In this presentation, brain networks are built from resting state functional magnetic resonance imaging (fMRI) and deterministic tractography. their results demonstrate that the proposed approach is capable of refining traditional heuristics, such as choosing the minimal distance from the lesion, by supplementing connectional importance of the resected tissue. This provides complementary information to help the surgeon in avoiding important functional hubs and their anatomical linkages; which are derived from neuroimaging modalities and incorporated to the related anatomical landmarks

Fast and cross-vendor OpenCL-based implementation for voxelization of triangular mesh models

As the open standard for parallel programming of heterogeneous systems, OpenCL has been used in this study in the context of a particular and intensive computing task, namely the voxelization of tessellated objects. For this purpose, OpenCL platform has been utilized to develop a parallelized voxelization algorithm that relies on a fast and efficient triangular mesh facet/cube overlapping test. The extensive numerical tests conducted with heterogeneous hardware configurations on geometric objects of varying complexities, mesh/domain sizes, and voxel resolutions suggest that up to 99.6% or 260 times decrease in the computation time can be obtained when GPU- or CPU-based parallelized techniques are used instead of the conventional single-thread CPU approach. Future developments will attempt the integration of the current implementation into a virtual orthopaedic surgery platform

A novel method for assessing visual perception of surgical planes

Background: Recognition of tissue planes during surgery appears to be a skill acquired with experience. We conducted a pilot study to test this hypothesis using a novel method for evaluating this skill in a simulated environment. Methods: Twelve surgeons of varying levels of experience were shown 16 captured images from a mesorectal excision. For each image, they were asked to draw the ideal dissection plane with a stylus on a tablet computer. We used a novel metric for comparing agreement between lines to determine the level of precision observed between junior and senior trainees and consultant surgeons and measure the accuracy of junior and senior trainees compared with consultant surgeons. Results: We observed significant differences in precision for 9 of 16 images; 7 of these followed the predicted stepwise pattern associated with level of experience. Using consultant surgeons as the reference standard, we observed significant differences in accuracy between senior and junior trainees for 11 images, with senior trainees being more accurate in 10 of them. Only 2 images failed to contribute significant findings to our analysis. Conclusion: The findings of this pilot evaluation of a novel method for measuring a surgeon\u27s ability to recognize tissue planes in a simulated model show that skill improves with experience. Further evaluation of this method will reveal its utility as an assessment tool and possibly as a training instrument

Cognitive intraindividual variability and white matter integrity in aging

The intraindividual variability (IIV) of cognitive performance has been shown to increase with aging. While brain research has generally focused on mean performance, little is known about neural correlates of cognitive IIV. Nevertheless, some studies suggest that IIV relates more strongly than mean level of performance to the quality of white matter (WM). Our study aims to explore the relation between WM integrity and cognitive IIV by combining functional (fMRI) and structural (diffusion tensor imaging, DTI) imaging. Twelve young adults (aged 18-30 years) and thirteen older adults (61-82 years) underwent a battery of neuropsychological tasks, along with fMRI and DTI imaging. Their behavioral data were analyzed and correlated with the imaging data at WM regions of interest defined on the basis of (1) the fMRI-activated areas and (2) the Johns Hopkins University (JHU) WM tractography atlas. For both methods, fractional anisotropy, along with the mean, radial, and axial diffusivity parameters, was computed. In accord with previous studies, our results showed that the DTI parameters were more related to IIV than to mean performance. Results also indicated that age differences in the DTI parameters were more pronounced in the regions activated primarily by young adults during a choice reaction-time task than in those also activated in older adults. © 2013 Nathalie Mella et al

Moderating Effect of Cortical Thickness on BOLD Signal Variability Age-Related Changes

The time course of neuroanatomical structural and functional measures across the lifespan is commonly reported in association with aging. Blood oxygen-level dependent signal variability, estimated using the standard deviation of the signal, or BOLDSD , is an emerging metric of variability in neural processing, and has been shown to be positively correlated with cognitive flexibility. Generally, BOLDSD is reported to decrease with aging, and is thought to reflect age-related cognitive decline. Additionally, it is well established that normative aging is associated with structural changes in brain regions, and that these predict functional decline in various cognitive domains. Nevertheless, the interaction between alterations in cortical morphology and BOLDSD changes has not been modeled quantitatively. The objective of the current study was to investigate the influence of cortical morphology metrics [i.e., cortical thickness (CT), gray matter (GM) volume, and cortical area (CA)] on age-related BOLDSD changes by treating these cortical morphology metrics as possible physiological confounds using linear mixed models. We studied these metrics in 28 healthy older subjects scanned twice at approximately 2.5 years interval. Results show that BOLDSD is confounded by cortical morphology metrics. Respectively, changes in CT but not GM volume nor CA, show a significant interaction with BOLDSD alterations. Our study highlights that CT changes should be considered when evaluating BOLDSD alternations in the lifespan

Perceptual Enhancement of Arteriovenous Malformation in MRI Angiography Displays

The importance of presenting medical images in an intuitive and usable manner during a procedure is essential. However, most medical visualization interfaces, particularly those designed for minimally-invasive surgery, suffer from a number of issues as a consequence of disregarding the human perceptual, cognitive, and motor system\u27s limitations. This matter is even more prominent when human visual system is overlooked during the design cycle. One example is the visualization of the neuro-vascular structures in MR angiography (MRA) images. This study investigates perceptual performance in the usability of a display to visualize blood vessels in MRA volumes using a contour enhancement technique. Our results show that when contours are enhanced, our participants, in general, can perform faster with higher level of accuracy when judging the connectivity of different vessels. One clinical outcome of such perceptual enhancement is improvement of spatial reasoning needed for planning complex neuro-vascular operations such as treating Arteriovenous Malformations (AVMs). The success of an AVM intervention greatly depends on fully understanding the anatomy of vascular structures. However, poor visualization of pre-operative MRA images makes the planning of such a treatment quite challenging