J. Sourati, D.H. Brooks, J.G. Dy, E. Erdogmus.
Constrained Spectral Clustering for Image Segmentation, In IEEE International Workshop on Machine Learning for Signal Processing, pp. 1--6. 2013.
Constrained spectral clustering with affinity propagation in its original form is not practical for large scale problems like image segmentation. In this paper we employ novelty selection sub-sampling strategy, besides using efficient numerical eigen-decomposition methods to make this algorithm work efficiently for images. In addition, entropy-based active learning is also employed to select the queries posed to the user more wisely in an interactive image segmentation framework. We evaluate the algorithm on general and medical images to show that the segmentation results will improve using constrained clustering even if one works with a subset of pixels. Furthermore, this happens more efficiently when pixels to be labeled are selected actively.
J. Sourati, K. Kose, M. Rajadhyaksha, J.G. Dy, D. Erdogmus, D.H. Brooks.
Automated localization of wrinkles and the dermo-epidermal junction in obliquely oriented reflectance confocal microscopic images of human skin, In Proc. SPIE 8565, Photonic Therapeutics and Diagnostics IX, Vol. 8565, 2013.
Reflectance Confocal Microscopic (RCM) imaging of obliquely-oriented optical sections, rather than with traditional z-stacks, shows depth information that more closely mimics the appearance of skin in orthogonal sections of histology. This approach may considerably reduce the amount of data that must be acquired and processed. However, as with z-stacks, purely visual detection of the dermal-epidermal junction (DEJ) in oblique images remains challenging. Here, we have extended our original algorithm for localization of DEJ in z-stacks to oblique images. In addition, we developed an algorithm for detecting wrinkles, which in addition to its intrinsic merit, gives useful information for DEJ detection.
G. Veni, Z. Fu, S.P. Awate, R.T. Whitaker.
Proper Ordered Meshing of Complex Shapes and Optimal Graph Cuts Applied to Atrial-Wall Segmentation from DE-MRI, In Proceedings of the 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI), pp. 1296--1299. 2013.
Segmentation of the left atrium wall from delayed enhancement MRI is challenging because of inconsistent contrast combined with noise and high variation in atrial shape and size. This paper presents a method for left-atrium wall segmentation by using a novel sophisticated mesh-generation strategy and graph cuts on a proper ordered graph. The mesh is part of a template/model that has an associated set of learned intensity features. When this mesh is overlaid onto a test image, it produces a set of costs on the graph vertices which eventually leads to an optimal segmentation. The novelty also lies in the construction of proper ordered graphs on complex shapes and for choosing among distinct classes of base shapes/meshes for automatic segmentation. We evaluate the proposed segmentation framework quantitatively on simulated and clinical cardiac MRI.
G. Veni, S. Awate, Z. Fu, R.T. Whittaker.
Bayesian Segmentation of Atrium Wall using Globally-Optimal Graph Cuts on 3D Meshes, In Proceedings of the International Conference on Information Processing in Medical Imaging (IPMI), Lecture Notes in Computer Science (LNCS), Vol. 23, pp. 656--677. 2013.
PubMed ID: 24684007
Efficient segmentation of the left atrium (LA) wall from delayed enhancement MRI is challenging due to inconsistent contrast, combined with noise, and high variation in atrial shape and size. We present a surface-detection method that is capable of extracting the atrial wall by computing an optimal a-posteriori estimate. This estimation is done on a set of nested meshes, constructed from an ensemble of segmented training images, and graph cuts on an associated multi-column, proper-ordered graph. The graph/mesh is a part of a template/model that has an associated set of learned intensity features. When this mesh is overlaid onto a test image, it produces a set of costs which lead to an optimal segmentation. The 3D mesh has an associated weighted, directed multi-column graph with edges that encode smoothness and inter-surface penalties. Unlike previous graph-cut methods that impose hard constraints on the surface properties, the proposed method follows from a Bayesian formulation resulting in soft penalties on spatial variation of the cuts through the mesh. The novelty of this method also lies in the construction of proper-ordered graphs on complex shapes for choosing among distinct classes of base shapes for automatic LA segmentation. We evaluate the proposed segmentation framework on simulated and clinical cardiac MRI.
Keywords: Atrial Fibrillation, Bayesian segmentation, Minimum s-t cut, Mesh Generation, Geometric Graph
D. Wang, R.M. Kirby, R.S. MacLeod, C.R. Johnson.
Inverse Electrocardiographic Source Localization of Ischemia: An Optimization Framework and Finite Element Solution, In Journal of Computational Physics, Vol. 250, Academic Press, pp. 403--424. 2013.
With the goal of non-invasively localizing cardiac ischemic disease using bodysurface potential recordings, we attempted to reconstruct the transmembrane potential (TMP) throughout the myocardium with the bidomain heart model. The task is an inverse source problem governed by partial differential equations (PDE). Our main contribution is solving the inverse problem within a PDE-constrained optimization framework that enables various physically-based constraints in both equality and inequality forms. We formulated the optimality conditions rigorously in the continuum before deriving finite element discretization, thereby making the optimization independent of discretization choice. Such a formulation was derived for the L2-norm Tikhonov regularization and the total variation minimization. The subsequent numerical optimization was fulfilled by a primal-dual interior-point method tailored to our problem's specific structure. Our simulations used realistic, fiberincluded heart models consisting of up to 18,000 nodes, much finer than any inverse models previously reported. With synthetic ischemia data we localized ischemic regions with roughly a 10% false-negative rate or a 20% false-positive rate under conditions up to 5% input noise. With ischemia data measured from animal experiments, we reconstructed TMPs with roughly 0.9 correlation with the ground truth. While precisely estimating the TMP in general cases remains an open problem, our study shows the feasibility of reconstructing TMP during the ST interval as a means of ischemia localization.
Keywords: cvrti, 2P41 GM103545-14
Fluorender, An Interactive Tool for Confocal Microscopy Data Visualization and Analysis, Note: Ph.D. Thesis, School of Computing, University of Utah, June, 2013.
Confocal microscopy has become a popular imaging technique in biology research in recent years. It is often used to study three-dimensional (3D) structures of biological samples. Confocal data are commonly multi-channel, with each channel resulting from a different fluorescent staining. This technique also results finely detailed structures in 3D, such as neuron fibers. Despite the plethora of volume rendering techniques that have been available for many years, there is a demand from biologists for a flexible tool that allows interactive visualization and analysis of multi-channel confocal data. Together with biologists, we have designed and developed FluoRender. It incorporates volume rendering techniques such as a two-dimensional (2D) transfer function and multi-channel intermixing. Rendering results can be enhanced through tone-mappings and overlays. To facilitate analyses of confocal data, FluoRender provides interactive operations for extracting complex structures. Furthermore, we developed the Synthetic Brainbow technique, which takes advantage of the asynchronous behavior in Graphics Processing Unit (GPU) framebuffer loops and generates random colorizations for different structures in single-channel confocal data. The results from our Synthetic Brainbows, when applied to a sequence of developing cells, can then be used for tracking the movements of these cells. Finally, we present an application of FluoRender in the workflow of constructing anatomical atlases.
Keywords: confocal microscopy, visualization, software
X. Zhu, Y. Gur, W. Wang, P.T. Fletcher.
Model Selection and Estimation of Multi-Compartment Models in Diffusion MRI with a Rician Noise Model, In Proceedings of the International Conference on Information Processing in Medical Imaging (IPMI), Lecture Notes in Computer Science (LNCS), Vol. 23, pp. 644--655. 2013.
PubMed ID: 24684006
Multi-compartment models in diffusion MRI (dMRI) are used to describe complex white matter fiber architecture of the brain. In this paper, we propose a novel multi-compartment estimation method based on the ball-and-stick model, which is composed of an isotropic diffusion compartment (\"ball\") as well as one or more perfectly linear diffusion compartments (\"sticks\"). To model the noise distribution intrinsic to dMRI measurements, we introduce a Rician likelihood term and estimate the model parameters by means of an Expectation Maximization (EM) algorithm. This paper also addresses the problem of selecting the number of fiber compartments that best fit the data, by introducing a sparsity prior on the volume mixing fractions. This term provides automatic model selection and enables us to discriminate different fiber populations. When applied to simulated data, our method provides accurate estimates of the fiber orientations, diffusivities, and number of compartments, even at low SNR, and outperforms similar methods that rely on a Gaussian noise distribution assumption. We also apply our method to in vivo
brain data and show that it can successfully capture complex fiber structures that match the known anatomy.
N.W. Akoum, C.J. McGann, G. Vergara, T. Badger, R. Ranjan, C. Mahnkopf, E.G. Kholmovski, R.S. Macleod, N.F. Marrouche.
Atrial Fibrosis Quantified Using Late Gadolinium Enhancement MRI is AssociatedWith Sinus Node Dysfunction Requiring Pacemaker Implant, In Journal of Cardiovascular Electrophysiology, Vol. 23, No. 1, pp. 44--50. 2012.
Atrial Fibrosis and Sinus Node Dysfunction. Introduction: Sinus node dysfunction (SND) commonly manifests with atrial arrhythmias alternating with sinus pauses and sinus bradycardia. The underlying process is thought to be because of atrial fibrosis. We assessed the value of atrial fibrosis, quantified using Late Gadolinium Enhanced-MRI (LGE-MRI), in predicting significant SND requiring pacemaker implant.
Methods: Three hundred forty-four patients with atrial fibrillation (AF) presenting for catheter ablation underwent LGE-MRI. Left atrial (LA) fibrosis was quantified in all patients and right atrial (RA) fibrosis in 134 patients. All patients underwent catheter ablation with pulmonary vein isolation with posterior wall and septal debulking. Patients were followed prospectively for 329 ± 245 days. Ambulatory monitoring was instituted every 3 months. Symptomatic pauses and bradycardia were treated with pacemaker implantation per published guidelines.
Results: The average patient age was 65 ± 12 years. The average wall fibrosis was 16.7 ± 11.1% in the LA, and 5.3 ± 6.4% in the RA. RA fibrosis was correlated with LA fibrosis (R2= 0.26; P < 0.01). Patients were divided into 4 stages of LA fibrosis (Utah I: 35%). Twenty-two patients (mean atrial fibrosis, 23.9%) required pacemaker implantation during follow-up. Univariate and multivariate analysis identified LA fibrosis stage (OR, 2.2) as a significant predictor for pacemaker implantation with an area under the curve of 0.704.
Conclusions: In patients with AF presenting for catheter ablation, LGE-MRI quantification of atrial fibrosis demonstrates preferential LA involvement. Significant atrial fibrosis is associated with clinically significant SND requiring pacemaker implantation. (J Cardiovasc Electrophysiol, Vol. 23, pp. 44-50, January 2012)
S.P. Awate, P. Zhu, R.T. Whitaker.
How Many Templates Does It Take for a Good Segmentation?: Error Analysis in Multiatlas Segmentation as a Function of Database Size, In Int. Workshop Multimodal Brain Image Analysis (MBIA) at Int. Conf. MICCAI, Lecture Notes in Computer Science (LNCS), Vol. 2, Note: Recieved Best Paper Award, pp. 103--114. 2012.
PubMed ID: 24501720
PubMed Central ID: PMC3910563
This paper proposes a novel formulation to model and analyze the statistical characteristics of some types of segmentation problems that are based on combining label maps / templates / atlases. Such segmentation-by-example approaches are quite powerful on their own for several clinical applications and they provide prior information, through spatial context, when combined with intensity-based segmentation methods. The proposed formulation models a class of multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of images. The paper presents a systematic analysis of the nonparametric estimation's convergence behavior (i.e. characterizing segmentation error as a function of the size of the multiatlas database) and shows that it has a specific analytic form involving several parameters that are fundamental to the specific segmentation problem (i.e. chosen anatomical structure, imaging modality, registration method, label-fusion algorithm, etc.). We describe how to estimate these parameters and show that several brain anatomical structures exhibit the trends determined analytically. The proposed framework also provides per-voxel confidence measures for the segmentation. We show that the segmentation error for large database sizes can be predicted using small-sized databases. Thus, small databases can be exploited to predict the database sizes required (\"how many templates\") to achieve \"good\" segmentations having errors lower than a specified tolerance. Such cost-benefit analysis is crucial for designing and deploying multiatlas segmentation systems.
J.R. Bronson, J.A. Levine, R.T. Whitaker.
Lattice Cleaving: Conforming Tetrahedral Meshes of Multimaterial Domains with Bounded Quality, In Proceedings of the 21st International Meshing Roundtable, pp. 191--209. 2012.
We introduce a new algorithm for generating tetrahedral meshes that conform to physical boundaries in volumetric domains consisting of multiple materials. The proposed method allows for an arbitrary number of materials, produces high-quality tetrahedral meshes with upper and lower bounds on dihedral angles, and guarantees geometric delity. Moreover, the method is combinatoric so its implementation enables rapid mesh construction. These meshes are structured in a way that also allows grading, in order to reduce element counts in regions of homogeneity.
J.R. Bronson, J.A. Levine, R.T. Whitaker.
Particle Systems for Adaptive, Isotropic Meshing of CAD Models, In Engineering with Computers, Vol. 28, No. 4, pp. 331--344. 2012.
PubMed ID: 23162181
PubMed Central ID: PMC3499137
We present a particle-based approach for generating adaptive triangular surface and tetrahedral volume meshes from computer-aided design models. Input shapes are treated as a collection of smooth, parametric surface patches that can meet non-smoothly on boundaries. Our approach uses a hierarchical sampling scheme that places particles on features in order of increasing dimensionality. These particles reach a good distribution by minimizing an energy computed in 3D world space, with movements occurring in the parametric space of each surface patch. Rather than using a pre-computed measure of feature size, our system automatically adapts to both curvature as well as a notion of topological separation. It also enforces a measure of smoothness on these constraints to construct a sizing field that acts as a proxy to piecewise-smooth feature size. We evaluate our technique with comparisons against other popular triangular meshing techniques for this domain.
C. Butson, G. Tamm, S. Jain, T. Fogal, J. Krüger.
Evaluation of Interactive Visualization on Mobile Computing Platforms for Selection of Deep Brain Stimulation Parameters, In IEEE Transactions on Visualization and Computer Graphics, pp. (accepted). 2012.
In recent years there has been significant growth in the use of patient-specific models to predict the effects of deep brain stimulation (DBS). However, translating these models from a research environment to the everyday clinical workflow has been a challenge. In this paper, we deploy the interactive visualization system ImageVis3D Mobile in an evaluation environment to visualize models of Parkinson’s disease patients who received DBS therapy. We used ImageVis3D Mobile to provide models to movement disorders clinicians and asked them to use the software to determine: 1) which of the four DBS electrode contacts they would select for therapy; and 2) what stimulation settings they would choose. We compared the stimulation protocol chosen from the software versus the stimulation protocol that was chosen via clinical practice (independently of the study). Lastly, we compared the amount of time required to reach these settings using the software versus the time required through standard practice. We found that the stimulation settings chosen using ImageVis3D Mobile were similar to those used in standard of care, but were selected in drastically less time. We show how our visualization system can be used to guide clinical decision making for selection of DBS settings.
Keywords: scidac, dbs
A.N.M. Imroz Choudhury, Bei Wang, P. Rosen, V. Pascucci.
Topological Analysis and Visualization of Cyclical Behavior in Memory Reference Traces, In Proceedings of the IEEE Pacific Visualization Symposium (PacificVis 2012), pp. 9--16. 2012.
We demonstrate the application of topological analysis techniques to the rather unexpected domain of software visualization. We collect a memory reference trace from a running program, recasting the linear flow of trace records as a high-dimensional point cloud in a metric space. We use topological persistence to automatically detect significant circular structures in the point cloud, which represent recurrent or cyclical runtime program behaviors. We visualize such recurrences using radial plots to display their time evolution, offering multi-scale visual insights, and detecting potential candidates for memory performance optimization. We then present several case studies to demonstrate some key insights obtained using our techniques.
M. Dannhauer, D.H. Brooks, D. Tucker, R.S. MacLeod.
A pipeline for the simulation of transcranial direct current stimulation for realistic human head models using SCIRun/BioMesh3D, In Proceedings of the 2012 IEEE Int. Conf. Engineering and Biology Society (EMBC), pp. 5486--5489. 2012.
PubMed ID: 23367171
PubMed Central ID: PMC3651514
The current work presents a computational pipeline to simulate transcranial direct current stimulation from image based models of the head with SCIRun . The pipeline contains all the steps necessary to carry out the simulations and is supported by a complete suite of open source software tools: image visualization, segmentation, mesh generation, tDCS electrode generation and efficient tDCS forward simulation.
M. Datar, P. Muralidharan, A. Kumar, S. Gouttard, J. Piven, G. Gerig, R.T. Whitaker, P.T. Fletcher.
Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy, In Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data, Lecture Notes in Computer Science, Vol. 7570, Springer Berlin / Heidelberg, pp. 76--87. 2012.
In this paper, we propose a new method for longitudinal shape analysis that ts a linear mixed-eects model, while simultaneously optimizing correspondences on a set of anatomical shapes. Shape changes are modeled in a hierarchical fashion, with the global population trend as a xed eect and individual trends as random eects. The statistical signi cance of the estimated trends are evaluated using speci cally designed permutation tests. We also develop a permutation test based on the Hotelling T2
statistic to compare the average shapes trends between two populations. We demonstrate the bene ts of our method on a synthetic example of longitudinal tori and data from a developmental neuroimaging study.
Keywords: Computer Science
B. Erem, P. Stovicek, D.H. Brooks.
Manifold learning for analysis of low-order nonlinear dynamics in high-dimensional electrocardiographic signals, In Proceedings of the 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 844--847. 2012.
The dynamical structure of electrical recordings from the heart or torso surface is a valuable source of information about cardiac physiological behavior. In this paper, we use an existing data-driven technique for manifold identification to reveal electrophysiologically significant changes in the underlying dynamical structure of these signals. Our results suggest that this analysis tool characterizes and differentiates important parameters of cardiac bioelectric activity through their dynamic behavior, suggesting the potential to serve as an effective dynamic constraint in the context of inverse solutions.
F.N. Golabchi, D.H. Brooks.
Axon segmentation in microscopy images - A graphical model based approach, In Proceedings of the 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 756-759. 2012.
Image segmentation of very large and complex microscopy images are challenging due to variability in the images and the need for algorithms to be robust, fast and able to incorporate various types of information and constraints in the segmentation model. In this paper we propose a graphical model based image segmentation framework that combines the information in images regions with the information in their boundary in a unified probabilistic formulation.
Y. Gur, F. Jiao, S.X. Zhu, C.R. Johnson.
White matter structure assessment from reduced HARDI data using low-rank polynomial approximations, In Proceedings of MICCAI 2012 Workshop on Computational Diffusion MRI (CDMRI12), Nice, France, Lecture Notes in Computer Science (LNCS), pp. 186-197. October, 2012.
Assessing white matter fiber orientations directly from DWI measurements in single-shell HARDI has many advantages. One of these advantages is the ability to model multiple fibers using fewer parameters than are required to describe an ODF and, thus, reduce the number of DW samples needed for the reconstruction. However, fitting a model directly to the data using Gaussian mixture, for instance, is known as an initialization-dependent unstable process. This paper presents a novel direct fitting technique for single-shell HARDI that enjoys the advantages of direct fitting without sacrificing the accuracy and stability even when the number of gradient directions is relatively low. This technique is based on a spherical deconvolution technique and decomposition of a homogeneous polynomial into a sum of powers of linear forms, known as a symmetric tensor decomposition
. The fiber-ODF (fODF), which is described by a homogeneous polynomial, is approximated here by a discrete sum of even-order linear-forms that are directly related to rank-1 tensors and represent single-fibers. This polynomial approximation is convolved to a single-fiber response function, and the result is optimized against the DWI measurements to assess the fiber orientations and the volume fractions directly. This formulation is accompanied by a robust iterative alternating numerical scheme which is based on the Levenberg- Marquardt technique. Using simulated data and in vivo, human brain data we show that the proposed algorithm is stable, accurate and can model complex fiber structures using only 12 gradient directions.
Biomedical Visual Computing: Case Studies and Challenges, In IEEE Computing in Science and Engineering, Vol. 14, No. 1, pp. 12--21. 2012.
PubMed ID: 22545005
PubMed Central ID: PMC3336198
Computer simulation and visualization are having a substantial impact on biomedicine and other areas of science and engineering. Advanced simulation and data acquisition techniques allow biomedical researchers to investigate increasingly sophisticated biological function and structure. A continuing trend in all computational science and engineering applications is the increasing size of resulting datasets. This trend is also evident in data acquisition, especially in image acquisition in biology and medical image databases.
For example, in a collaboration between neuroscientist Robert Marc and our research team at the University of Utah's Scientific Computing and Imaging (SCI) Institute (www.sci.utah.edu), we're creating datasets of brain electron microscopy (EM) mosaics that are 16 terabytes in size. However, while there's no foreseeable end to the increase in our ability to produce simulation data or record observational data, our ability to use this data in meaningful ways is inhibited by current data analysis capabilities, which already lag far behind. Indeed, as the NIH-NSF Visualization Research Challenges report notes, to effectively understand and make use of the vast amounts of data researchers are producing is one of the greatest scientific challenges of the 21st century.
Visual data analysis involves creating images that convey salient information about underlying data and processes, enabling the detection and validation of expected results while leading to unexpected discoveries in science. This allows for the validation of new theoretical models, provides comparison between models and datasets, enables quantitative and qualitative querying, improves interpretation of data, and facilitates decision making. Scientists can use visual data analysis systems to explore \"what if\" scenarios, define hypotheses, and examine data under multiple perspectives and assumptions. In addition, they can identify connections between numerous attributes and quantitatively assess the reliability of hypotheses. In essence, visual data analysis is an integral part of scientific problem solving and discovery.
As applied to biomedical systems, visualization plays a crucial role in our ability to comprehend large and complex data-data that, in two, three, or more dimensions, convey insight into many diverse biomedical applications, including understanding neural connectivity within the brain, interpreting bioelectric currents within the heart, characterizing white-matter tracts by diffusion tensor imaging, and understanding morphology differences among different genetic mice phenotypes.
J. Knezevic, R.-P. Mundani, E. Rank, A. Khan, C.R. Johnson.
Extending the SCIRun Problem Solving Environment to Large-Scale Applications, In Proceedings of Applied Computing 2012, IADIS, pp. 171--178. October, 2012.
To make the most of current advanced computing technologies, experts in particular areas of science and engineering should be supported by sophisticated tools for carrying out computational experiments. The complexity of individual components of such tools should be hidden from them so they may concentrate on solving the specific problem within their field of expertise. One class of such tools are Problem Solving Environments (PSEs). The contribution of this paper refers to the idea of integration of an interactive computing framework applicable to different engineering applications into the SCIRun
PSE in order to enable interactive real-time response of the computational model to user interaction even for large-scale problems. While the SCIRun
PSE allows for real-time computational steering, we propose extending this functionality to a wider range of applications and larger scale problems. With only minor code modifications the proposed system allows each module scheduled for execution in a dataflow-based simulation to be automatically interrupted and re-scheduled. This rescheduling allows one to keep the relation between the user interaction and its immediate effect transparent independent of the problem size, thus, allowing for the intuitive and interactive exploration of simulation results.