M.D. Harris, M. Datar, R.T. Whitaker, E.R. Jurrus, C.L. Peters, A.E. Anderson.
Statistical Shape Modeling of Cam Femoroacetabular Impingement, In Journal of Orthopaedic Research, Vol. 31, No. 10, pp. 1620--1626. 2013.
Statistical shape modeling (SSM) was used to quantify 3D variation and morphologic differences between femurs with and without cam femoroacetabular impingement (FAI). 3D surfaces were generated from CT scans of femurs from 41 controls and 30 cam FAI patients. SSM correspondence particles were optimally positioned on each surface using a gradient descent energy function. Mean shapes for groups were defined. Morphological differences between group mean shapes and between the control mean and individual patients were calculated. Principal component analysis described anatomical variation. Among all femurs, the first six modes (or principal components) captured significant variations, which comprised 84% of cumulative variation. The first two modes, which described trochanteric height and femoral neck width, were significantly different between groups. The mean cam femur shape protruded above the control mean by a maximum of 3.3 mm with sustained protrusions of 2.5–3.0 mm along the anterolateral head-neck junction/distal anterior neck. SSM described variations in femoral morphology that corresponded well with areas prone to damage. Shape variation described by the first two modes may facilitate objective characterization of cam FAI deformities; variation beyond may be inherent population variance. SSM could characterize disease severity and guide surgical resection of bone.
H. Hernandez, J. Knezevic, T. Fogal, T. Sherman, T. Jevremovic.
Visual numerical steering in 3D AGENT code system for advanced nuclear reactor modeling and design, In Annals of Nuclear Energy, Vol. 55, pp. 248--257. 2013.
The AGENT simulation system is used for detailed three-dimensional modeling of neutron transport and corresponding properties of nuclear reactors of any design. Numerical solution to the neutron transport equation in the AGENT system is based on the Method of Characteristics (MOCs) and the theory of R-functions. The latter of which is used for accurately describing current and future heterogeneous lattices of reactor core configurations. The AGENT code has been extensively verified to assure a high degree of accuracy for predicting neutron three-dimensional point-wise flux spatial distributions, power peaking factors, reaction rates, and eigenvalues. In this paper, a new AGENT code feature, a computational steering, is presented. This new feature provides a novel way for using deterministic codes for fast evaluation of reactor core parameters, at no loss to accuracy. The computational steering framework as developed at the Technische Universität München is smoothly integrated into the AGENT solver. This framework allows for an arbitrary interruption of AGENT simulation, allowing the solver to restart with updated parameters. One possible use of this is to accelerate the convergence of the final values resulting in significantly reduced simulation times. Using this computational steering in the AGENT system, coarse MOC resolution parameters can initially be selected and later update them – while the simulation is actively running – into fine resolution parameters. The utility of the steering framework is demonstrated using the geometry of a research reactor at the University of Utah: this new approach provides a savings in CPU time on the order of 50%.
Keywords: Numerical steering, AGENT code, Deterministic neutron transport codes, Method of Characteristics, R-functions, Numerical visualizations
K. Higuchi, M. Akkaya, M. Koopmann, J.J. Blauer, N.S. Burgon, K. Damal, R. Ranjan, E. Kholmovski, R.S. Macleod, N.F. Marrouche..
The Effect of Fat Pad Modification during Ablation of Atrial Fibrillation: Late Gadolinium Enhancement MRI Analysis, In Pacing and Clinical Electrophysiology (PACE), Vol. 36, No. 4, pp. 467--476. April, 2013.
PubMed ID: 23356963
PubMed Central ID: PMC3651513
Background: Magnetic resonance imaging (MRI) can visualize locations of both the ablation scar on the left atrium (LA) after atrial fibrillation (AF) ablation and epicardial fat pads (FPs) containing ganglionated plexi (GP).
Methods: We investigated 60 patients who underwent pulmonary vein antrum (PVA) isolation along with LA posterior wall and septal debulking for AF. FPs around the LA surface in well-known GP areas (which were considered as the substitution of GP areas around the LA) were segmented from the dark-blood MRI. Then the FP and the ablation scar image visualized by late gadolinium enhancement (LGE)-MRI on the LA were merged together. Overlapping areas of FP and the ablation scar image were considered as the ablated FP areas containing GP. Patients underwent 24-hour Holter monitoring after ablation for the analysis of heart rate variability.
Results: Ablated FP area was significantly wider in patients without AF recurrence than those in patients with recurrence (5.6 ± 3.1 cm2 vs 4.2 ± 2.7 cm2 ,P = 0.03). The mean values of both percentage of differences greater than 50 ms in the RR intervals (pRR > 50) and standard deviation of RR intervals over the entire analyzed period (SDNN), which were obtained from 24-hour Holter monitoring 1-day post-AF ablation, were significantly lower in patients without recurrence than those in patients with recurrence (5.8 ± 6.0% vs 14.0 ± 10.1%; P = 0.0005, 78.7 ± 32.4 ms vs 109.2 ± 43.5 ms; P = 0.005). There was a significant negative correlation between SDNN and the percentage of ablated FP area (Y =- 1.3168X + 118.96, R2 = 0.1576, P = 0.003).
Conclusion: Extensively ablating LA covering GP areas along with PVA isolation enhanced the denervation of autonomic nerve system and seemed to improve procedural outcome in patients with AF.
Keywords: ganglionated plexi, fat pad, atrial fibrillation, catheter ablation, LGE-MRI
F. Jiao, J.M. Phillips, Y. Gur, C.R. Johnson.
Uncertainty Visualization in HARDI based on Ensembles of ODFs, In Proceedings of 2013 IEEE Pacific Visualization Symposium, pp. 193--200. 2013.
PubMed ID: 24466504
PubMed Central ID: PMC3898522
In this paper, we propose a new and accurate technique for uncertainty analysis and uncertainty visualization based on fiber orientation distribution function (ODF) glyphs, associated with high angular resolution diffusion imaging (HARDI). Our visualization applies volume rendering techniques to an ensemble of 3D ODF glyphs, which we call SIP functions of diffusion shapes, to capture their variability due to underlying uncertainty. This rendering elucidates the complex heteroscedastic structural variation in these shapes. Furthermore, we quantify the extent of this variation by measuring the fraction of the volume of these shapes, which is consistent across all noise levels, the certain volume ratio. Our uncertainty analysis and visualization framework is then applied to synthetic data, as well as to HARDI human-brain data, to study the impact of various image acquisition parameters and background noise levels on the diffusion shapes.
C.R. Johnson, A. Pang (Eds.).
International Journal for Uncertainty Quantification, Subtitled Special Issue on Working with Uncertainty: Representation, Quantification, Propagation, Visualization, and Communication of Uncertainty, In Int. J. Uncertainty Quantification, Vol. 3, No. 2, Begell House, Inc., pp. vii--viii. 2013.
C.R. Johnson, A. Pang (Eds.).
International Journal for Uncertainty Quantification, Subtitled Special Issue on Working with Uncertainty: Representation, Quantification, Propagation, Visualization, and Communication of Uncertainty, In Int. J. Uncertainty Quantification, Vol. 3, No. 3, Begell House, Inc., 2013.
C. Jones, M. Seyedhosseini, M. Ellisman, T. Tasdizen.
Neuron Segmentation in Electron Microscopy Images Using Partial Differential Equations, In Proceedings of 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI), pp. 1457--1460. April, 2013.
In connectomics, neuroscientists seek to identify the synaptic connections between neurons. Segmentation of cell membranes using supervised learning algorithms on electron microscopy images of brain tissue is often done to assist in this effort. Here we present a partial differential equation with a novel growth term to improve the results of a supervised learning algorithm. We also introduce a new method for representing the resulting image that allows for a more dynamic thresholding to further improve the result. Using these two processes we are able to close small to medium sized gaps in the cell membrane detection and improve the Rand error by as much as 9\% over the initial supervised segmentation.
K.B. Jones, M. Datar, S. Ravichandran, H. Jin, E. Jurrus, R.T. Whitaker, M.R. Capecchi.
Toward an Understanding of the Short Bone Phenotype Associated with Multiple Osteochondromas, In Journal of Orthopaedic Research, Vol. 31, No. 4, pp. 651--657. 2013.
PubMed ID: 23192691
PubMed Central ID: PMC3683979
Individuals with multiple osteochondromas (MO) demonstrate shortened long bones. Ext1 or Ext2 haploinsufficiency cannot recapitulate the phenotype in mice. Loss of heterozygosity for Ext1 may induce shortening by steal of longitudinal growth into osteochondromas or by a general derangement of physeal signaling. We induced osteochondromagenesis at different time points during skeletal growth in a mouse genetic model, then analyzed femora and tibiae at 12 weeks using micro-CT and a point-distribution-based shape analysis. Bone lengths and volumes were compared. Metaphyseal volume deviations from normal, as a measure of phenotypic widening, were tested for correlation with length deviations. Mice with osteochondromas had shorter femora and tibiae than controls, more consistently when osteochondromagenesis was induced earlier during skeletal growth. Volumetric metaphyseal widening did not correlate with longitudinal shortening, although some of the most severe shortening was in bones with abundant osteochondromas. Loss of heterozygosity for Ext1 was sufficient to drive bone shortening in a mouse model of MO, but shortening did not correlate with osteochondroma volumetric growth. While a steal phenomenon seems apparent in individual cases, some other mechanism must also be capable of contributing to the short bone phenotype, independent of osteochondroma formation. Clones of chondrocytes lacking functional heparan sulfate must blunt physeal signaling generally, rather than stealing growth potential focally. © 2012 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 31: 651-657, 2013.
K.S. McDowell, F. Vadakkumpadan, R. Blake, J. Blauer, G.t Plank, R.S. MacLeod, N.A. Trayanova.
Mechanistic Inquiry into the Role of Tissue Remodeling in Fibrotic Lesions in Human Atrial Fibrillation, In Biophysical Journal, Vol. 104, pp. 2764--2773. 2013.
PubMed ID: 23790385
PubMed Central ID: PMC3686346
Atrial fibrillation (AF), the most common arrhythmia in humans, is initiated when triggered activity from the pulmonary veins propagates into atrial tissue and degrades into reentrant activity. Although experimental and clinical findings show a correlation between atrial fibrosis and AF, the causal relationship between the two remains elusive. This study used an array of 3D computational models with different representations of fibrosis based on a patient-specific atrial geometry with accurate fibrotic distribution to determine the mechanisms by which fibrosis underlies the degradation of a pulmonary vein ectopic beat into AF. Fibrotic lesions in models were represented with combinations of: gap junction remodeling; collagen deposition; and myofibroblast proliferation with electrotonic or paracrine effects on neighboring myocytes. The study found that the occurrence of gap junction remodeling and the subsequent conduction slowing in the fibrotic lesions was a necessary but not sufficient condition for AF development, whereas myofibroblast proliferation and the subsequent electrophysiological effect on neighboring myocytes within the fibrotic lesions was the sufficient condition necessary for reentry formation. Collagen did not alter the arrhythmogenic outcome resulting from the other fibrosis components. Reentrant circuits formed throughout the noncontiguous fibrotic lesions, without anchoring to a specific fibrotic lesion.
C. McGann, N. Akoum, A. Patel, E. Kholmovski, P. Revelo, K. Damal, B. Wilson, J. Cates, A. Harrison, R. Ranjan, N.S. Burgon, T. Greene, D. Kim, E.V.R. DiBella, D. Parker, R.S. MacLeod, N.F. Marrouche.
Atrial Fibrillation Ablation Outcome is Predicted by Left Atrial Remodeling on MRI, In Circulation: Arrhythmia and Electrophysiology, Note: Published online before print., December, 2013.
Background: While catheter ablation therapy for atrial fibrillation (AF) is becoming more common, results vary widely and patient selection criteria remain poorly defined. We hypothesized that late gadolinium enhancement magnetic resonance imaging (LGE-MRI) can identify left atrial (LA) wall structural remodeling (SRM) and stratify patients who are likely or not to benefit from ablation therapy. Methods and Results:
LGE-MRI was performed on 426 consecutive AF patients without contraindications to MRI and before undergoing their first ablation procedure and on 21 non-AF control subjects. Patients were categorized by SRM stage (I-IV) based on percentage of LA wall enhancement for correlation with procedure outcomes. Histological validation of SRM was performed comparing LGE-MRI to surgical biopsy. A total of 386 patients (91%) with adequate LGE-MRI scans were included in the study. Post-ablation, 123 (31.9%) experienced recurrent atrial arrhythmias over one-year follow-up. Recurrent arrhythmias (failed ablations) occurred at higher SRM stages with 28/133 (21.0%) stage I, 40/140 (29.3%) stage II, 24/71 (33.8%) stage III, and 30/42 (71.4%) stage IV. In multi-variate analysis, ablation outcome was best predicted by advanced SRM stage (hazard ratio (HR) 4.89; p
Keywords: atrial fibrillation arrhythmia, catheter ablation, magnetic resonance imaging, remodeling, outcome
R.M. Orellana, B. Erem, D.H. Brooks.
Time invariant multi electrode averaging for biomedical signals, In Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1242--1246. 2013.
One of the biggest challenges in averaging ECG or EEG signals is to overcome temporal misalignments and distortions, due to uncertain timing or complex non-stationary dynamics. Standard methods average individual leads over a collection of epochs on a time-sample by time-sample basis, even when multi-electrode signals are available. Here we propose a method that averages multi electrode recordings simultaneously by using spatial patterns and without relying on time or frequency.
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.