Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Large scale visualization on the Powerwall.
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

SCI Publications

2015


P. T. Bremer, D. Maljovec, A. Saha, Bei Wang, J. Gaffney, B. K. Spears, V. Pascucci. “ND2AV: N-Dimensional Data Analysis and Visualization -- Analysis for the National Ignition Campaign,” In Computing and Visualization in Science, 2015.

ABSTRACT

One of the biggest challenges in high-energy physics is to analyze a complex mix of experimental and simulation data to gain new insights into the underlying physics. Currently, this analysis relies primarily on the intuition of trained experts often using nothing more sophisticated than default scatter plots. Many advanced analysis techniques are not easily accessible to scientists and not flexible enough to explore the potentially interesting hypotheses in an intuitive manner. Furthermore, results from individual techniques are often difficult to integrate, leading to a confusing patchwork of analysis snippets too cumbersome for data exploration. This paper presents a case study on how a combination of techniques from statistics, machine learning, topology, and visualization can have a significant impact in the field of inertial confinement fusion. We present the ND2AV: N-Dimensional Data Analysis and Visualization framework, a user-friendly tool aimed at exploiting the intuition and current work flow of the target users. The system integrates traditional analysis approaches such as dimension reduction and clustering with state-of-the-art techniques such as neighborhood graphs and topological analysis, and custom capabilities such as defining combined metrics on the fly. All components are linked into an interactive environment that enables an intuitive exploration of a wide variety of hypotheses while relating the results to concepts familiar to the users, such as scatter plots. ND2AV uses a modular design providing easy extensibility and customization for different applications. ND2AV is being actively used in the National Ignition Campaign and has already led to a number of unexpected discoveries.



T. Bregman, R. Reznikov, M. Diwan, R. Raymond, C. R.Butson, J. N.Nobrega, C. Hamani. “Antidepressant-like Effects of Medial Forebrain Bundle Deep Brain Stimulation in Rats are not Associated With Accumbens Dopamine Release,” In Brain Stimulation, Vol. 8, No. 4, pp. 708--713. 2015.

ABSTRACT

BACKGROUND:
Medial forebrain bundle (MFB) deep brain stimulation (DBS) is currently being investigated in patients with treatment-resistant depression. Striking features of this therapy are the large number of patients who respond to treatment and the rapid nature of the antidepressant response.

OBJECTIVE:
To study antidepressant-like behavioral responses, changes in regional brain activity, and monoamine release in rats receiving MFB DBS.

METHODS:
Antidepressant-like effects of MFB stimulation at 100 μA, 90 μs and either 130 Hz or 20 Hz were characterized in the forced swim test (FST). Changes in the expression of the immediate early gene (IEG) zif268 were measured with in situ hybridization and used as an index of regional brain activity. Microdialysis was used to measure DBS-induced dopamine and serotonin release in the nucleus accumbens.

RESULTS:
Stimulation at parameters that approximated those used in clinical practice, but not at lower frequencies, induced a significant antidepressant-like response in the FST. In animals receiving MFB DBS at high frequency, increases in zif268 expression were observed in the piriform cortex, prelimbic cortex, nucleus accumbens shell, anterior regions of the caudate/putamen and the ventral tegmental area. These structures are involved in the neurocircuitry of reward and are also connected to other brain areas via the MFB. At settings used during behavioral tests, stimulation did not induce either dopamine or serotonin release in the nucleus accumbens.

CONCLUSIONS:
These results suggest that MFB DBS induces an antidepressant-like effect in rats and recruits structures involved in the neurocircuitry of reward without affecting dopamine release in the nucleus accumbens.



C. R. Butson, C. C. McIntyre. “The use of stimulation field models for deep brain stimulation programming,” In Brain Stimulation, Vol. 8, No. 5, Elsevier BV, pp. 976--978. September, 2015.
DOI: 10.1016/j.brs.2015.06.005



H. Carr, Z. Geng, J. Tierny, A. Chattophadhyay,, A. Knoll. “Fiber Surfaces: Generalizing Isosurfaces to Bivariate Data,” In Computer Graphics Forum, Vol. 34, No. 3, pp. 241-250. 2015.

ABSTRACT

Scientific visualization has many effective methods for examining and exploring scalar and vector fields, but rather fewer for multi-variate fields. We report the first general purpose approach for the interactive extraction of geometric separating surfaces in bivariate fields. This method is based on fiber surfaces: surfaces constructed from sets of fibers, the multivariate analogues of isolines. We show simple methods for fiber surface definition and extraction. In particular, we show a simple and efficient fiber surface extraction algorithm based on Marching Cubes. We also show how to construct fiber surfaces interactively with geometric primitives in the range of the function. We then extend this to build user interfaces that generate parameterized families of fiber surfaces with respect to arbitrary polylines and polygons. In the special case of isovalue-gradient plots, fiber surfaces capture features geometrically for quantitative analysis that have previously only been analysed visually and qualitatively using multi-dimensional transfer functions in volume rendering. We also demonstrate fiber surface extraction on a variety of bivariate data



CIBC. Note: Data Sets: NCRR Center for Integrative Biomedical Computing (CIBC) data set archive. Download from: http://www.sci.utah.edu/cibc/software.html, 2015.



CIBC. Note: Cleaver: A MultiMaterial Tetrahedral Meshing Library and Application. Scientific Computing and Imaging Institute (SCI), Download from: http://www.sci.utah.edu/cibc/software.html, 2015.



C.C. Conlin, J.L. Zhang, F. Rousset, C. Vachet, Y. Zhao, K.A. Morton, K. Carlston, G. Gerig, V.S. Lee. “Performance of an Efficient Image-registration Algorithm in Processing MR Renography Data,” In J Magnetic Resonance Imaging, July, 2015.
DOI: 10.1002/jmri.25000

ABSTRACT

PURPOSE:
To evaluate the performance of an edge-based registration technique in correcting for respiratory motion artifacts in magnetic resonance renographic (MRR) data and to examine the efficiency of a semiautomatic software package in processing renographic data from a cohort of clinical patients.

MATERIALS AND METHODS:
The developed software incorporates an image-registration algorithm based on the generalized Hough transform of edge maps. It was used to estimate glomerular filtration rate (GFR), renal plasma flow (RPF), and mean transit time (MTT) from 36 patients who underwent free-breathing MRR at 3T using saturation-recovery turbo-FLASH. The processing time required for each patient was recorded. Renal parameter estimates and model-fitting residues from the software were compared to those from a previously reported technique. Interreader variability in the software was quantified by the standard deviation of parameter estimates among three readers. GFR estimates from our software were also compared to a reference standard from nuclear medicine.

RESULTS:
The time taken to process one patient's data with the software averaged 12 ± 4 minutes. The applied image registration effectively reduced motion artifacts in dynamic images by providing renal tracer-retention curves with significantly smaller fitting residues (P < 0.01) than unregistered data or data registered by the previously reported technique. Interreader variability was less than 10% for all parameters. GFR estimates from the proposed method showed greater concordance with reference values (P < 0.05).

CONCLUSION:
These results suggest that the proposed software can process MRR data efficiently and accurately. Its incorporated registration technique based on the generalized Hough transform effectively reduces respiratory motion artifacts in free-breathing renographic acquisitions. J. Magn. Reson. Imaging 2015.



S. Durrleman, T.P. Fletcher, G. Gerig, M. Niethammer, X. Pennec (Eds.). “Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data,” In Proceedings of the Third International Workshop, STIA 2014, Image Processing, Computer Vision, Pattern Recognition, and Graphics, Vol. 8682, Springer LNCS, 2015.
ISBN: 978-3-319-14905-9

ABSTRACT

This book constitutes the thoroughly refereed post-conference proceedings of the Third
International Workshop on Spatio-temporal Image Analysis for Longitudinal and Time-
Series Image Data, STIA 2014, held in conjunction with MICCAI 2014 in Boston, MA, USA, in
September 2014.

The 7 papers presented in this volume were carefully reviewed and selected from 15
submissions. They are organized in topical sections named: longitudinal registration and
shape modeling, longitudinal modeling, reconstruction from longitudinal data, and 4D
image processing.



J. Edwards, S. Kumar, V. Pascucci. “Big data from scientific simulations,” In Big Data and High Performance Computing, Vol. 26, IOS Press, pp. 32. 2015.

ABSTRACT

Scienti c simulations often generate massive amounts of data used for debugging, restarts, and scienti c analysis and discovery. Challenges that practitioners face using these types of big data are unique. Of primary importance is speed of writing data during a simulation, but this need for fast I/O is at odds with other priorities, such as data access time for visualization and analysis, ecient storage, and portability across a variety of supercomputer topologies, con gurations, le systems, and storage devices. The computational power of high-performance computing systems continues to increase according to Moore's law, but the same is not true for I/O subsystems, creating a performance gap between computation and I/O. This chapter explores these issues, as well as possible optimization strategies, the use of in situ analytics, and a case study using the PIDX I/O library in a typical simulation.



J. Edwards, E. Daniel, V. Pascucci, C. Bajaj. “Approximating the Generalized Voronoi Diagram of Closely Spaced Objects,” In Computer Graphics Forum, Vol. 34, No. 2, Wiley-Blackwell, pp. 299-309. May, 2015.
DOI: 10.1111/cgf.12561

ABSTRACT

Generalized Voronoi Diagrams (GVDs) have far-reaching applications in robotics, visualization, graphics, and simulation. However, while the ordinary Voronoi Diagram has mature and efficient algorithms for its computation, the GVD is difficult to compute in general, and in fact, has only approximation algorithms for anything but the simplest of datasets. Our work is focused on developing algorithms to compute the GVD efficiently and with bounded error on the most difficult of datasets -- those with objects that are extremely close to each other.



E. Erdil, A.O. Argunsah, T. Tasdizen, D. Unay, M. Cetin. “A joint classification and segmentation approach for dendritic spine segmentation in 2-photon microscopy images,” In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), IEEE, April, 2015.
DOI: 10.1109/isbi.2015.7163992

ABSTRACT

Shape priors have been successfully used in challenging biomedical imaging problems. However when the shape distribution involves multiple shape classes, leading to a multimodal shape density, effective use of shape priors in segmentation becomes more challenging. In such scenarios, knowing the class of the shape can aid the segmentation process, which is of course unknown a priori. In this paper, we propose a joint classification and segmentation approach for dendritic spine segmentation which infers the class of the spine during segmentation and adapts the remaining segmentation process accordingly. We evaluate our proposed approach on 2-photon microscopy images containing dendritic spines and compare its performance quantitatively to an existing approach based on nonparametric shape priors. Both visual and quantitative results demonstrate the effectiveness of our approach in dendritic spine segmentation.



T. Etiene, R.M. Kirby, C. Silva. “An Introduction to Verification of Visualization Techniques,” Morgan & Claypool Publishers, 2015.



SCI Institute. Note: FluoRender: An interactive rendering tool for confocal microscopy data visualization. Scientific Computing and Imaging Institute (SCI) Download from: http://www.fluorender.org, 2015.



Note: FusionView: Problem Solving Environment for MHD Visualization. Scientific Computing and Imaging Institute (SCI), Download from: http://www.scirun.org, 2015.



Y. Gao, L. Zhu, J. Cates, R. S. MacLeod, S. Bouix,, A. Tannenbaum. “A Kalman Filtering Perspective for Multiatlas Segmentation,” In SIAM J. Imaging Sciences, Vol. 8, No. 2, pp. 1007-1029. 2015.
DOI: 10.1137/130933423

ABSTRACT

In multiatlas segmentation, one typically registers several atlases to the novel image, and their respective segmented label images are transformed and fused to form the final segmentation. In this work, we provide a new dynamical system perspective for multiatlas segmentation, inspired by the following fact: The transformation that aligns the current atlas to the novel image can be not only computed by direct registration but also inferred from the transformation that aligns the previous atlas to the image together with the transformation between the two atlases. This process is similar to the global positioning system on a vehicle, which gets position by inquiring from the satellite and by employing the previous location and velocity—neither answer in isolation being perfect. To solve this problem, a dynamical system scheme is crucial to combine the two pieces of information; for example, a Kalman filtering scheme is used. Accordingly, in this work, a Kalman multiatlas segmentation is proposed to stabilize the global/affine registration step. The contributions of this work are twofold. First, it provides a new dynamical systematic perspective for standard independent multiatlas registrations, and it is solved by Kalman filtering. Second, with very little extra computation, it can be combined with most existing multiatlas segmentation schemes for better registration/segmentation accuracy.



M.U. Ghani, S.D. Kanik, A.O. Argunsah, T. Tasdizen, D. Unay, M. Cetin. “Dendritic spine shape classification from two-photon microscopy images,” In 2015 23nd Signal Processing and Communications Applications Conference (SIU), IEEE, May, 2015.
DOI: 10.1109/siu.2015.7129985

ABSTRACT

Functional properties of a neuron are coupled with its morphology, particularly the morphology of dendritic spines. Spine volume has been used as the primary morphological parameter in order the characterize the structure and function coupling. However, this reductionist approach neglects the rich shape repertoire of dendritic spines. First step to incorporate spine shape information into functional coupling is classifying main spine shapes that were proposed in the literature. Due to the lack of reliable and fully automatic tools to analyze the morphology of the spines, such analysis is often performed manually, which is a laborious and time intensive task and prone to subjectivity. In this paper we present an automated approach to extract features using basic image processing techniques, and classify spines into mushroom or stubby by applying machine learning algorithms. Out of 50 manually segmented mushroom and stubby spines, Support Vector Machine was able to classify 98% of the spines correctly.



K. Gillette, J.D. Tate, B. Kindall, P. Van Dam, E. Kholmovski, R.S. MacLeod. “Generation of Combined-Modality Tetrahedral Meshes,” In Computing in Cardiology, 2015.

ABSTRACT

Registering and combining anatomical components from different image modalities, like MRI and CT that have different tissue contrast, could result in patient-specific models that more closely represent underlying anatomical structures.

In this study, we combined a pair of CT and MRI scans of a pig thorax to make a tetrahedral mesh and compared different registration techniques including rigid, affine, thin plate spline morphing (TPSM), and iterative closest point (ICP), to  superimpose the segmented bones from the CT scan on the soft tissues segmented from the MRI. The TPSM and affine-registered bones remained close to, but not overlapping, important soft tissue.

Simulation models, including an ECG forward model and a defibrillation model, were computed on generated multi-modality meshes after TPSM and affine registration and compared to those based on the original torso mesh.



B. D. Goodwin, C. R. Butson. “Subject-Specific Multiscale Modeling to Investigate Effects of Transcranial Magnetic Stimulation,” In Neuromodulation: Technology at the Neural Interface, Vol. 18, No. 8, Wiley-Blackwell, pp. 694--704. May, 2015.
DOI: 10.1111/ner.12296

ABSTRACT

OBJECTIVE:
Transcranial magnetic stimulation (TMS) is an effective intervention in noninvasive neuromodulation used to treat a number of neurophysiological disorders. Predicting the spatial extent to which neural tissue is affected by TMS remains a challenge. The goal of this study was to develop a computational model to predict specific locations of neural tissue that are activated during TMS. Using this approach, we assessed the effects of changing TMS coil orientation and waveform.

MATERIALS AND METHODS:
We integrated novel techniques to develop a subject-specific computational model, which contains three main components: 1) a figure-8 coil (Magstim, Magstim Company Limited, Carmarthenshire, UK); 2) an electromagnetic, time-dependent, nonhomogeneous, finite element model of the whole head; and 3) an adaptation of a previously published pyramidal cell neuron model. We then used our modeling approach to quantify the spatial extent of affected neural tissue for changes in TMS coil rotation and waveform.

RESULTS:
We found that our model shows more detailed predictions than previously published models, which underestimate the spatial extent of neural activation. Our results suggest that fortuitous sites of neural activation occur for all tested coil orientations. Additionally, our model predictions show that excitability of individual neural elements changes with a coil rotation of ±15°.

CONCLUSIONS:
Our results indicate that the extent of neuromodulation is more widespread than previous published models suggest. Additionally, both specific locations in cortex and the extent of stimulation in cortex depend on coil orientation to within ±15° at a minimum. Lastly, through computational means, we are able to provide insight into the effects of TMS at a cellular level, which is currently unachievable by imaging modalities.



C. Gritton, M. Berzins, R. M. Kirby. “Improving Accuracy In Particle Methods Using Null Spaces and Filters,” In Proceedings of the IV International Conference on Particle-Based Methods - Fundamentals and Applications, Barcelona, Spain, Edited by E. Onate and M. Bischoff and D.R.J. Owen and P. Wriggers and T. Zohdi, CIMNE, pp. 202-213. September, 2015.
ISBN: 978-84-944244-7-2

ABSTRACT

While particle-in-cell type methods, such as MPM, have been very successful in providing solutions to many challenging problems there are some important issues that remain to be resolved with regard to their analysis. One such challenge relates to the difference in dimensionality between the particles and the grid points to which they are mapped. There exists a non-trivial null space of the linear operator that maps particles values onto nodal values. In other words, there are non-zero particle values values that when mapped to the nodes are zero there. Given positive mapping weights such null space values are oscillatory in nature. The null space may be viewed as a more general form of the ringing instability identified by Brackbill for PIC methods. It will be shown that it is possible to remove these null-space values from the solution and so to improve the accuracy of PIC methods, using a matrix SVD approach. The expense of doing this is prohibitive for real problems and so a local method is developed for doing this.



A. V. P. Grosset, M. Prasad, C. Christensen, A. Knoll, C. Hansen. “TOD-Tree: Task-Overlapped Direct send Tree Image Compositing for Hybrid MPI Parallelism,” In Eurographics Symposium on Parallel Graphics and Visualization (2015), Edited by C. Dachsbacher, P. Navrátil, 2015.

ABSTRACT

Modern supercomputers have very powerful multi-core CPUs. The programming model on these supercomputer is switching from pure MPI to MPI for inter-node communication, and shared memory and threads for intra-node communication. Consequently the bottleneck in most systems is no longer computation but communication between nodes. In this paper, we present a new compositing algorithm for hybrid MPI parallelism that focuses on communication avoidance and overlapping communication with computation at the expense of evenly balancing the workload. The algorithm has three stages: a direct send stage where nodes are arranged in groups and exchange regions of an image, followed by a tree compositing stage and a gather stage. We compare our algorithm with radix-k and binary-swap from the IceT library in a hybrid OpenMP/MPI setting, show strong scaling results and explain how we generally achieve better performance than these two algorithms.