2015

J. Bennett, F. Vivodtzev, V. Pascucci (Eds.).
**“Topological and Statistical Methods for Complex Data,”** Subtitled **“Tackling Large-Scale, High-Dimensional, and Multivariate Data Spaces,”** Mathematics and Visualization, 2015.

ISBN: 978-3-662-44899-1

This book contains papers presented at the Workshop on the Analysis of Large-scale,

High-Dimensional, and Multi-Variate Data Using Topology and Statistics, held in Le Barp,

France, June 2013. It features the work of some of the most prominent and recognized

leaders in the field who examine challenges as well as detail solutions to the analysis of

extreme scale data.

The book presents new methods that leverage the mutual strengths of both topological

and statistical techniques to support the management, analysis, and visualization

of complex data. It covers both theory and application and provides readers with an

overview of important key concepts and the latest research trends.

Coverage in the book includes multi-variate and/or high-dimensional analysis techniques,

feature-based statistical methods, combinatorial algorithms, scalable statistics algorithms,

scalar and vector field topology, and multi-scale representations. In addition, the book

details algorithms that are broadly applicable and can be used by application scientists to

glean insight from a wide range of complex data sets.

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.

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

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.

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.

CIBC.
Note: *ImageVis3D: An interactive visualization software system for large-scale volume data. Scientific Computing and Imaging Institute (SCI), Download from: http://www.imagevis3d.org*, 2015.

M. Kim, C.D. Hansen.
**“Surface Flow Visualization using the Closest Point Embedding,”** In *2015 IEEE Pacific Visualization Symposium*, April, 2015.

In this paper, we introduce a novel flow visualization technique for arbitrary surfaces. This new technique utilizes the closest point embedding to represent the surface, which allows for accurate particle advection on the surface as well as supports the unsteady flow line integral convolution (UFLIC) technique on the surface. This global approach is faster than previous parameterization techniques and prevents the visual artifacts associated with image-based approaches.

**Keywords:** vector field, flow visualization

M. Kim, C.D. Hansen.
**“GPU Surface Extraction with the Closest Point Embedding,”** In *Proceedings of IS&T/SPIE Visualization and Data Analysis, 2015*, February, 2015.

Isosurface extraction is a fundamental technique used for both surface reconstruction and mesh generation. One method to extract well-formed isosurfaces is a particle system; unfortunately, particle systems can be slow. In this paper, we introduce an enhanced parallel particle system that uses the closest point embedding as the surface representation to speedup the particle system for isosurface extraction. The closest point embedding is used in the Closest Point Method (CPM), a technique that uses a standard three dimensional numerical PDE solver on two dimensional embedded surfaces. To fully take advantage of the closest point embedding, it is coupled with a Barnes-Hut tree code on the GPU. This new technique produces well-formed, conformal unstructured triangular and tetrahedral meshes from labeled multi-material volume datasets. Further, this new parallel implementation of the particle system is faster than any known methods for conformal multi-material mesh extraction. The resulting speed-ups gained in this implementation can reduce the time from labeled data to mesh from hours to minutes and benefits users, such as bioengineers, who employ triangular and tetrahedral meshes.

**Keywords:** scalar field methods, GPGPU, curvature based, scientific visualization

CIBC.
Note: *map3d: Interactive scientific visualization tool for bioengineering data. Scientific Computing and Imaging Institute (SCI), Download from: http://www.sci.utah.edu/cibc/software.html*, 2015.

SCI Institute.
Note: *NCR Toolset: A collection of software tools for the reconstruction and visualization of neural circuitry from electron microscopy data. Scientific Computing and Imaging Institute (SCI). Download from: http://www.sci.utah.edu/software.html*, 2015.

J.R. Pruett Jr., S. Kandala, S. Hoertel, A.Z. Snyder, J.T. Elison, T. Nishino, E. Feczko, N.U.F. Dosenbach, B. Nardos, J.D. Power, B. Adeyemo, K.N. Botteron, R.C. McKinstry, A.C. Evans, H.C. Hazlett, S.R. Dager, S. Paterson, R.T. Schultz, D.L. Collins, V.S. Fonov, M. Styner, G. Gerig, S. Das, P. Kostopoulos, J.N. Constantino, A.M. Estes, The IBIS Network, S.E. Petersen, B.L. Schlaggar, J. Piven.
**“Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data,”** In *Developmental Cognitive Neuroscience*, Vol. 12, pp. 123--133. April, 2015.

DOI: 10.1016/j.dcn.2015.01.003

Human large-scale functional brain networks are hypothesized to undergo significant changes over development. Little is known about these functional architectural changes, particularly during the second half of the first year of life. We used multivariate pattern classification of resting-state functional connectivity magnetic resonance imaging (fcMRI) data obtained in an on-going, multi-site, longitudinal study of brain and behavioral development to explore whether fcMRI data contained information sufficient to classify infant age. Analyses carefully account for the effects of fcMRI motion artifact. Support vector machines (SVMs) classified 6 versus 12 month-old infants (128 datasets) above chance based on fcMRI data alone. Results demonstrate significant changes in measures of brain functional organization that coincide with a special period of dramatic change in infant motor, cognitive, and social development. Explorations of the most different correlations used for SVM lead to two different interpretations about functional connections that support 6 versus 12-month age categorization.

Note: *Scientific Computing and Imaging Institute (SCI), University of Utah, www.sci.utah.edu*, 2015.

SCI Institute.
Note: *SCIRun: A Scientific Computing Problem Solving Environment, Scientific Computing and Imaging Institute (SCI), Download from: http://www.scirun.org*, 2015.

CIBC.
Note: *Seg3D: Volumetric Image Segmentation and Visualization. Scientific Computing and Imaging Institute (SCI), Download from: http://www.seg3d.org*, 2015.

SCI Institute.
Note: *ShapeWorks: An open-source tool for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on specific surface parameterization. Scientific Computing and Imaging Institute (SCI). Download from: http://www.sci.utah.edu/software/shapeworks.html*, 2015.

SLASH.
Note: *SLASH: A hybrid system for high-throughput segmentation of large neuropil datasets, SLASH is funded by the National Institute of Neurological Disorders and Stroke (NINDS) grant 5R01NS075314-03.*, 2015.

Note: *VisTrails: A scientific workflow management system. Scientific Computing and Imaging Institute (SCI), Download from: http://www.vistrails.org*, 2015.

2014

G. Adluru, Y. Gur, J. Anderson, L. Richards, N. Adluru, E. DiBella.
**“Assessment of white matter microstructure in stroke patients using NODDI,”** In *Proceedings of the 2014 IEEE Int. Conf. Engineering and Biology Society (EMBC)*, 2014.

Diffusion weighted imaging (DWI) is widely used to study changes in white matter following stroke. In various studies employing diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI) modalities, it has been shown that fractional anisotropy (FA), mean diffusivity (MD), and generalized FA (GFA) can be used as measures of white matter tract integrity in stroke patients. However, these measures may be non-specific, as they do not directly delineate changes in tissue microstructure. Multi-compartment models overcome this limitation by modeling DWI data using a set of indices that are directly related to white matter microstructure. One of these models which is gaining popularity, is neurite orientation dispersion and density imaging (NODDI). This model uses conventional single or multi-shell HARDI data to describe fiber orientation dispersion as well as densities of different tissue types in the imaging voxel. In this paper, we apply for the first time the NODDI model to 4-shell HARDI stroke data. By computing NODDI indices over the entire brain in two stroke patients, and comparing tissue regions in ipsilesional and contralesional hemispheres, we demonstrate that NODDI modeling provides specific information on tissue microstructural changes. We also introduce an information theoretic analysis framework to investigate the non-local effects of stroke in the white matter. Our initial results suggest that the NODDI indices might be more specific markers of white matter reorganization following stroke than other measures previously used in studies of stroke recovery.

S.P. Awate, R.T. Whitaker.
**“Multiatlas Segmentation as Nonparametric Regression,”** In *IEEE Trans Med Imaging*, April, 2014.

PubMed ID: 24802528

This paper proposes a novel theoretical framework to model and analyze the statistical characteristics of a wide range of segmentation methods that incorporate a database of label maps or atlases; such methods are termed as label fusion or multiatlas segmentation. We model these multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of image patches. We analyze the nonparametric estimator's convergence behavior that characterizes expected segmentation error as a function of the size of the multiatlas database. We show that this error has an analytic form involving several parameters that are fundamental to the specific segmentation problem (determined by the chosen anatomical structure, imaging modality, registration algorithm, and labelfusion algorithm). We describe how to estimate these parameters and show that several human anatomical structures exhibit the trends modeled analytically. We use these parameter estimates to optimize the regression estimator. We show that the expected error for large database sizes is well predicted by models learned on small databases. Thus, a few expert segmentations can help predict the database sizes required to keep the expected error below a specified tolerance level. Such cost-benefit analysis is crucial for deploying clinical multiatlas segmentation systems.