Publications by Year: 2010

2010
Georg Langs, Polina Golland, Yanmei Tie, Laura Rigolo, and Alexandra J Golby. 2010. “Functional Geometry Alignment and Localization of Brain Areas.” Adv Neural Inf Process Syst, 1, Pp. 1225-1233.Abstract
Matching functional brain regions across individuals is a challenging task, largely due to the variability in their location and extent. It is particularly difficult, but highly relevant, for patients with pathologies such as brain tumors, which can cause substantial reorganization of functional systems. In such cases spatial registration based on anatomical data is only of limited value if the goal is to establish correspondences of functional areas among different individuals, or to localize potentially displaced active regions. Rather than rely on spatial alignment, we propose to perform registration in an alternative space whose geometry is governed by the functional interaction patterns in the brain. We first embed each brain into a functional map that reflects connectivity patterns during a fMRI experiment. The resulting functional maps are then registered, and the obtained correspondences are propagated back to the two brains. In application to a language fMRI experiment, our preliminary results suggest that the proposed method yields improved functional correspondences across subjects. This advantage is pronounced for subjects with tumors that affect the language areas and thus cause spatial reorganization of the functional regions.
Bjoern H Menze, Koen Van Leemput, Danial Lashkari, Marc-André Weber, Nicholas Ayache, and Polina Golland. 2010. “A generative model for brain tumor segmentation in multi-modal images.” Med Image Comput Comput Assist Interv, 13, Pt 2, Pp. 151-9.Abstract
We introduce a generative probabilistic model for segmentation of tumors in multi-dimensional images. The model allows for different tumor boundaries in each channel, reflecting difference in tumor appearance across modalities. We augment a probabilistic atlas of healthy tissue priors with a latent atlas of the lesion and derive the estimation algorithm to extract tumor boundaries and the latent atlas from the image data. We present experiments on 25 glioma patient data sets, demonstrating significant improvement over the traditional multivariate tumor segmentation.
Mert R Sabuncu, Thomas BT Yeo, Koen Van Leemput, Bruce Fischl, and Polina Golland. 2010. “A generative model for image segmentation based on label fusion.” IEEE Trans Med Imaging, 29, 10, Pp. 1714-29.Abstract
We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute the final segmentation of the test subject. Such label fusion methods have been shown to yield accurate segmentation, since the use of multiple registrations captures greater inter-subject anatomical variability and improves robustness against occasional registration failures. To the best of our knowledge, this manuscript presents the first comprehensive probabilistic framework that rigorously motivates label fusion as a segmentation approach. The proposed framework allows us to compare different label fusion algorithms theoretically and practically. In particular, recent label fusion or multiatlas segmentation algorithms are interpreted as special cases of our framework. We conduct two sets of experiments to validate the proposed methods. In the first set of experiments, we use 39 brain MRI scans-with manually segmented white matter, cerebral cortex, ventricles and subcortical structures-to compare different label fusion algorithms and the widely-used FreeSurfer whole-brain segmentation tool. Our results indicate that the proposed framework yields more accurate segmentation than FreeSurfer and previous label fusion algorithms. In a second experiment, we use brain MRI scans of 282 subjects to demonstrate that the proposed segmentation tool is sufficiently sensitive to robustly detect hippocampal volume changes in a study of aging and Alzheimer's Disease.
Samuel Dambreville, Romeil Sandhu, Anthony Yezzi, and Allen Tannenbaum. 2010. “A Geometric Approach to Joint 2D Region-Based Segmentation and 3D Pose Estimation Using a 3D Shape Prior.” SIAM J Imaging Sci, 3, 1, Pp. 110-132.Abstract
In this work, we present an approach to jointly segment a rigid object in a two-dimensional (2D) image and estimate its three-dimensional (3D) pose, using the knowledge of a 3D model. We naturally couple the two processes together into a shape optimization problem and minimize a unique energy functional through a variational approach. Our methodology differs from the standard monocular 3D pose estimation algorithms since it does not rely on local image features. Instead, we use global image statistics to drive the pose estimation process. This confers a satisfying level of robustness to noise and initialization for our algorithm and bypasses the need to establish correspondences between image and object features. Moreover, our methodology possesses the typical qualities of region-based active contour techniques with shape priors, such as robustness to occlusions or missing information, without the need to evolve an infinite dimensional curve. Another novelty of the proposed contribution is to use a unique 3D model surface of the object, instead of learning a large collection of 2D shapes to accommodate the diverse aspects that a 3D object can take when imaged by a camera. Experimental results on both synthetic and real images are provided, which highlight the robust performance of the technique in challenging tracking and segmentation applications.
Nathalie YR Agar, James G Malcolm, Vandana Mohan, Hong W Yang, Mark D Johnson, Allen Tannenbaum, Jeffrey N Agar, and Peter M Black. 2010. “Imaging of meningioma progression by matrix-assisted laser desorption ionization time-of-flight mass spectrometry.” Anal Chem, 82, 7, Pp. 2621-5.Abstract
Often considered benign, meningiomas represent 32% of intracranial tumors with three grades of malignancy defined by the World Health Organization (WHO) histology based classification. Malignant meningiomas are associated with less than 2 years median survival. The inability to predict recurrence and progression of meningiomas induces significant anxiety for patients and limits physicians in implementing prophylactic treatment approaches. This report presents an analytical approach to tissue characterization based on matrix-assisted laser desorption ionization time-of-flight (MALDI TOF) mass spectrometry imaging (MSI) which is introduced in an attempt to develop a reference database for predictive classification of brain tumors. This pilot study was designed to evaluate the potential of such an approach and to begin to address limitations of the current methodology. Five recurrent and progressive meningiomas for which surgical specimens were available from the original and progressed grades were selected and tested against nonprogressive high-grade meningiomas, high-grade gliomas, and nontumor brain specimens. The common profiling approach of data acquisition was compared to imaging and revealed significant benefits in spatially resolved acquisition for improved spectral definition. A preliminary classifier based on the support vector machine showed the ability to distinguish meningioma image spectra from the nontumor brain and from gliomas, a different type of brain tumor, and to enable class imaging of surgical tissue. Although the development of classifiers was shown to be sensitive to data preparation parameters such as recalibration and peak picking criteria, it also suggested the potential for maturing into a predictive algorithm if provided with a larger series of well-defined cases.
Junichi Tokuda, Gregory S Fischer, Simon P Dimaio, David G Gobbi, Csaba Csoma, Philip W Mewes, Gabor Fichtinger, Clare M Tempany, and Nobuhiko Hata. 2010. “Integrated navigation and control software system for MRI-guided robotic prostate interventions.” Comput Med Imaging Graph, 34, 1, Pp. 3-8.Abstract
A software system to provide intuitive navigation for MRI-guided robotic transperineal prostate therapy is presented. In the system, the robot control unit, the MRI scanner, and the open-source navigation software are connected together via Ethernet to exchange commands, coordinates, and images using an open network communication protocol, OpenIGTLink. The system has six states called "workphases" that provide the necessary synchronization of all components during each stage of the clinical workflow, and the user interface guides the operator linearly through these workphases. On top of this framework, the software provides the following features for needle guidance: interactive target planning; 3D image visualization with current needle position; treatment monitoring through real-time MR images of needle trajectories in the prostate. These features are supported by calibration of robot and image coordinates by fiducial-based registration. Performance tests show that the registration error of the system was 2.6mm within the prostate volume. Registered real-time 2D images were displayed 1.97 s after the image location is specified.
Archana Venkataraman, Yogesh Rathi, Marek Kubicki, Carl-Fredrik Westin, and Polina Golland. 2010. “Joint generative model for fMRI/DWI and its application to population studies.” Med Image Comput Comput Assist Interv, 13, Pt 1, Pp. 191-9.Abstract
We propose a novel probabilistic framework to merge information from DWI tractography and resting-state fMRI correlations. In particular, we model the interaction of latent anatomical and functional connectivity templates between brain regions and present an intuitive extension to population studies. We employ a mean-field approximation to fit the new model to the data. The resulting algorithm identifies differences in latent connectivity between the groups. We demonstrate our method on a study of normal controls and schizophrenia patients.
Thomas BT Yeo, Mert R Sabuncu, Tom Vercauteren, Daphne J Holt, Katrin Amunts, Karl Zilles, Polina Golland, and Bruce Fischl. 2010. “Learning task-optimal registration cost functions for localizing cytoarchitecture and function in the cerebral cortex.” IEEE Trans Med Imaging, 29, 7, Pp. 1424-41.Abstract
Image registration is typically formulated as an optimization problem with multiple tunable, manually set parameters. We present a principled framework for learning thousands of parameters of registration cost functions, such as a spatially-varying tradeoff between the image dissimilarity and regularization terms. Our approach belongs to the classic machine learning framework of model selection by optimization of cross-validation error. This second layer of optimization of cross-validation error over and above registration selects parameters in the registration cost function that result in good registration as measured by the performance of the specific application in a training data set. Much research effort has been devoted to developing generic registration algorithms, which are then specialized to particular imaging modalities, particular imaging targets and particular postregistration analyses. Our framework allows for a systematic adaptation of generic registration cost functions to specific applications by learning the "free" parameters in the cost functions. Here, we consider the application of localizing underlying cytoarchitecture and functional regions in the cerebral cortex by alignment of cortical folding. Most previous work assumes that perfectly registering the macro-anatomy also perfectly aligns the underlying cortical function even though macro-anatomy does not completely predict brain function. In contrast, we learn 1) optimal weights on different cortical folds or 2) optimal cortical folding template in the generic weighted sum of squared differences dissimilarity measure for the localization task. We demonstrate state-of-the-art localization results in both histological and functional magnetic resonance imaging data sets.
Peter Savadjiev, Gordon L Kindlmann, Sylvain Bouix, Martha E Shenton, and Carl-Fredrik Westin. 2010. “Local white matter geometry from diffusion tensor gradients.” Neuroimage, 49, 4, Pp. 3175-86.Abstract
We introduce a mathematical framework for computing geometrical properties of white matter fibers directly from diffusion tensor fields. The key idea is to isolate the portion of the gradient of the tensor field corresponding to local variation in tensor orientation, and to project it onto a coordinate frame of tensor eigenvectors. The resulting eigenframe-centered representation then makes it possible to define scalar indices (or measures) that describe the local white matter geometry directly from the diffusion tensor field and its gradient, without requiring prior tractography. We derive new scalar indices of (1) fiber dispersion and (2) fiber curving, and we demonstrate them on synthetic and in vivo data. Finally, we illustrate their applicability to a group study on schizophrenia.
Jong Woo Lee, Patrick Y Wen, Shelley Hurwitz, Peter Black, Santosh Kesari, Jan Drappatz, Alexandra J Golby, William M Wells, Simon K Warfield, Ron Kikinis, and Edward B Bromfield. 2010. “Morphological characteristics of brain tumors causing seizures.” Arch Neurol, 67, 3, Pp. 336-42.Abstract
OBJECTIVE: To quantify size and localization differences between tumors presenting with seizures vs nonseizure neurological symptoms. DESIGN: Retrospective imaging survey. We performed magnetic resonance imaging-based morphometric analysis and nonparametric mapping in patients with brain tumors. SETTING: University-affiliated teaching hospital. PATIENTS OR OTHER PARTICIPANTS: One hundred twenty-four patients with newly diagnosed supratentorial glial tumors. MAIN OUTCOME MEASURES: Volumetric and mapping methods were used to evaluate differences in size and location of the tumors in patients who presented with seizures as compared with patients who presented with other symptoms. RESULTS: In high-grade gliomas, tumors presenting with seizures were smaller than tumors presenting with other neurological symptoms, whereas in low-grade gliomas, tumors presenting with seizures were larger. Tumor location maps revealed that in high-grade gliomas, deep-seated tumors in the pericallosal regions were more likely to present with nonseizure neurological symptoms. In low-grade gliomas, tumors of the temporal lobe as well as the insular region were more likely to present with seizures. CONCLUSIONS: The influence of size and location of the tumors on their propensity to cause seizures varies with the grade of the tumor. In high-grade gliomas, rapidly growing tumors, particularly those situated in deeper structures, present with non-seizure-related symptoms. In low-grade gliomas, lesions in the temporal lobe or the insula grow large without other symptoms and eventually cause seizures. Quantitative image analysis allows for the mapping of regions in each group that are more or less susceptible to seizures.
Thomas Schultz, Carl-Fredrik Westin, and Gordon Kindlmann. 2010. “Multi-diffusion-tensor fitting via spherical deconvolution: a unifying framework.” Med Image Comput Comput Assist Interv, 13, Pt 1, Pp. 674-81.Abstract
In analyzing diffusion magnetic resonance imaging, multi-tensor models address the limitations of the single diffusion tensor in situations of partial voluming and fiber crossings. However, selection of a suitable number of fibers and numerical difficulties in model fitting have limited their practical use. This paper addresses both problems by making spherical deconvolution part of the fitting process: We demonstrate that with an appropriate kernel, the deconvolution provides a reliable approximative fit that is efficiently refined by a subsequent descent-type optimization. Moreover, deciding on the number of fibers based on the orientation distribution function produces favorable results when compared to the traditional F-Test. Our work demonstrates the benefits of unifying previously divergent lines of work in diffusion image analysis.
Wanmei Ou, Aapo Nummenmaa, Jyrki Ahveninen, John W Belliveau, Matti S Hämäläinen, and Polina Golland. 2010. “Multimodal functional imaging using fMRI-informed regional EEG/MEG source estimation.” Neuroimage, 52, 1, Pp. 97-108.Abstract
We propose a novel method, fMRI-Informed Regional Estimation (FIRE), which utilizes information from fMRI in E/MEG source reconstruction. FIRE takes advantage of the spatial alignment between the neural and the vascular activities, while allowing for substantial differences in their dynamics. Furthermore, with a region-based approach, FIRE estimates the model parameters for each region independently. Hence, it can be efficiently applied on a dense grid of source locations. The optimization procedure at the core of FIRE is related to the re-weighted minimum-norm algorithms. The weights in the proposed approach are computed from both the current source estimates and fMRI data, leading to robust estimates in the presence of silent sources in either fMRI or E/MEG measurements. We employ a Monte Carlo evaluation procedure to compare the proposed method to several other joint E/MEG-fMRI algorithms. Our results show that FIRE provides the best trade-off in estimation accuracy between the spatial and the temporal accuracy. Analysis using human E/MEG-fMRI data reveals that FIRE significantly reduces the ambiguities in source localization present in the minimum-norm estimates, and that it accurately captures activation timing in adjacent functional regions.
Yosef A Berlow, William M Wells, James M Ellison, Young Hoon Sung, Perry F Renshaw, and David G Harper. 2010. “Neuropsychiatric correlates of white matter hyperintensities in Alzheimer's disease.” Int J Geriatr Psychiatry, 25, 8, Pp. 780-8.Abstract
OBJECTIVE: To investigate the association of behavioral and psychological symptoms of dementia (BPSD) in Alzheimer's disease (AD) and magnetic resonance imaging (MRI) measures of brain atrophy and white matter hyperintensities (WMH). METHODS: Thirty-seven patients with probable AD received the Neuropsychiatric Inventory (NPI), the Mini Mental Status Exam (MMSE), and an MRI scan as part of their initial evaluation at the Outpatient Memory Diagnostic Clinic at McLean Hospital. MRI-based volumetric measurements of whole brain atrophy, hippocampal volumes, and WMH were obtained. Analysis of covariance models, using age as a covariate and the presence of specific BPSD as independent variables, were used to test for differences in whole brain volumes, hippocampal volumes and WMH volumes. RESULTS: Increased WMH were associated with symptoms of anxiety, aberrant motor behavior, and night time disturbance, while symptoms of disinhibition were linked to lower WMH volume. No associations were found for whole brain or hippocampal volumes and BPSD. CONCLUSIONS: These findings suggest that white matter changes are associated with the presence of BPSD in AD.
Danial Lashkari, Ramesh Sridharan, Edward Vul, Po-Jang Hsieh, Nancy Kanwisher, and Polina Golland. 2010. “Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation.” Conf Comput Vis Pattern Recognit Workshops, Pp. 15-22.Abstract
We develop a method for unsupervised analysis of functional brain images that learns group-level patterns of functional response. Our algorithm is based on a generative model that comprises two main layers. At the lower level, we express the functional brain response to each stimulus as a binary activation variable. At the next level, we define a prior over the sets of activation variables in all subjects. We use a Hierarchical Dirichlet Process as the prior in order to simultaneously learn the patterns of response that are shared across the group, and to estimate the number of these patterns supported by data. Inference based on this model enables automatic discovery and characterization of salient and consistent patterns in functional signals. We apply our method to data from a study that explores the response of the visual cortex to a collection of images. The discovered profiles of activation correspond to selectivity to a number of image categories such as faces, bodies, and scenes. More generally, our results appear superior to the results of alternative data-driven methods in capturing the category structure in the space of stimuli.
Romeil Sandhu, Samuel Dambreville, and Allen Tannenbaum. 2010. “Point set registration via particle filtering and stochastic dynamics.” IEEE Trans Pattern Anal Mach Intell, 32, 8, Pp. 1459-73.Abstract
In this paper, we propose a particle filtering approach for the problem of registering two point sets that differ by a rigid body transformation. Typically, registration algorithms compute the transformation parameters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem, in which the corresponding distribution can naturally be estimated using a particle filter. In this work, we treat motion as a local variation in pose parameters obtained by running a few iterations of a certain local optimizer. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence often found in local optimizer approaches for registration. Thus, the novelty of our method is threefold: First, we employ a particle filtering scheme to drive the point set registration process. Second, we present a local optimizer that is motivated by the correlation measure. Third, we increase the robustness of the registration performance by introducing a dynamic model of uncertainty for the transformation parameters. In contrast with other techniques, our approach requires no annealing schedule, which results in a reduction in computational complexity (with respect to particle size) as well as maintains the temporal coherency of the state (no loss of information). Also unlike some alternative approaches for point set registration, we make no geometric assumptions on the two data sets. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures, and/or differing point densities in each set, on several challenging 2D and 3D registration scenarios.
Behnood Gholami, Wassim M Haddad, and Allen R Tannenbaum. 2010. “Relevance vector machine learning for neonate pain intensity assessment using digital imaging.” IEEE Trans Biomed Eng, 57, 6, Pp. 1457-66.Abstract
Pain assessment in patients who are unable to verbally communicate is a challenging problem. The fundamental limitations in pain assessment in neonates stem from subjective assessment criteria, rather than quantifiable and measurable data. This often results in poor quality and inconsistent treatment of patient pain management. Recent advancements in pattern recognition techniques using relevance vector machine (RVM) learning techniques can assist medical staff in assessing pain by constantly monitoring the patient and providing the clinician with quantifiable data for pain management. The RVM classification technique is a Bayesian extension of the support vector machine (SVM) algorithm, which achieves comparable performance to SVM while providing posterior probabilities for class memberships and a sparser model. If classes represent "pure" facial expressions (i.e., extreme expressions that an observer can identify with a high degree of confidence), then the posterior probability of the membership of some intermediate facial expression to a class can provide an estimate of the intensity of such an expression. In this paper, we use the RVM classification technique to distinguish pain from nonpain in neonates as well as assess their pain intensity levels. We also correlate our results with the pain intensity assessed by expert and nonexpert human examiners.
Michal Depa, Mert R Sabuncu, Godtfred Holmvang, Reza Nezafat, Ehud J Schmidt, and Polina Golland. 2010. “Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation.” Stat Atlases Comput Models Heart, 6364, Pp. 85-94.Abstract
Automatic segmentation of the heart's left atrium offers great benefits for planning and outcome evaluation of atrial ablation procedures. However, the high anatomical variability of the left atrium presents significant challenges for atlas-guided segmentation. In this paper, we demonstrate an automatic method for left atrium segmentation using weighted voting label fusion and a variant of the demons registration algorithm adapted to handle images with different intensity distributions. We achieve accurate automatic segmentation that is robust to the high anatomical variations in the shape of the left atrium in a clinical dataset of MRA images.
Archana Venkataraman, Marek Kubicki, Carl-Fredrik Westin, and Polina Golland. 2010. “Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies.” Conf Comput Vis Pattern Recognit Workshops, Pp. 63-70.Abstract
We propose an alternative to univariate statistics for identifying population differences in functional connectivity. Our feature selection method is based on a procedure that searches across subsets of the data to isolate a set of robust, predictive functional connections. The metric, known as the Gini Importance, also summarizes multivariate patterns of interaction, which cannot be captured by univariate techniques. We compare the Gini Importance with univariate statistical tests to evaluate functional connectivity changes induced by schizophrenia. Our empirical results indicate that univariate features vary dramatically across subsets of the data and have little classification power. In contrast, relevant features based on Gini Importance are considerably more stable and allow us to accurately predict the diagnosis of a test subject.
DS Meier, KE Balashov, B Healy, HL Weiner, and CRG Guttmann. 2010. “Seasonal prevalence of MS disease activity.” Neurology, 75, 9, Pp. 799-806.Abstract
OBJECTIVE: This observational cohort study investigated the seasonal prevalence of multiple sclerosis (MS) disease activity (likelihood and intensity), as reflected by new lesions from serial T2-weighted MRI, a sensitive marker of subclinical disease activity. METHODS: Disease activity was assessed from the appearance of new T2 lesions on 939 separate brain MRI examinations in 44 untreated patients with MS. Likelihood functions for MS disease activity were derived, accounting for the temporal uncertainty of new lesion occurrence, individual levels of disease activity, and uneven examination intervals. Both likelihood and intensity of disease activity were compared with the time of year (season) and regional climate data (temperature, solar radiation, precipitation) and among relapsing and progressive disease phenotypes. Contrast-enhancing lesions and attack counts were also compared for seasonal effects. RESULTS: Unlike contrast enhancement or attacks, new T2 activity revealed a likelihood 2-3 times higher in March-August than during the rest of the year, and correlated strongly with regional climate data, in particular solar radiation. In addition to the likelihood or prevalence, disease intensity was also elevated during the summer season. The elevated risk season appears to lessen for progressive MS and occur about 2 months earlier. CONCLUSION: This study documents evidence of a strong seasonal pattern in subclinical MS activity based on noncontrast brain MRI. The observed seasonality in MS disease activity has implications for trial design and therapy assessment. The observed activity pattern is suggestive of a modulating role of seasonally changing environmental factors or season-dependent metabolic activity.
Tammy Riklin-Raviv, Koen Van Leemput, Bjoern H Menze, William M Wells, and Polina Golland. 2010. “Segmentation of image ensembles via latent atlases.” Med Image Anal, 14, 5, Pp. 654-65.Abstract
Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. However, the availability of comprehensive, reliable and suitable manual segmentations for atlas construction is limited. We therefore propose a method for joint segmentation of corresponding regions of interest in a collection of aligned images that does not require labeled training data. Instead, a latent atlas, initialized by at most a single manual segmentation, is inferred from the evolving segmentations of the ensemble. The algorithm is based on probabilistic principles but is solved using partial differential equations (PDEs) and energy minimization criteria. We evaluate the method on two datasets, segmenting subcortical and cortical structures in a multi-subject study and extracting brain tumors in a single-subject multi-modal longitudinal experiment. We compare the segmentation results to manual segmentations, when those exist, and to the results of a state-of-the-art atlas-based segmentation method. The quality of the results supports the latent atlas as a promising alternative when existing atlases are not compatible with the images to be segmented.

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