Automatic Segmentation of the Left Atrium from MRI Images using Salient Feature and Contour Evolution
L. Zhu, Y. Gao, A. Yezzi, R.S. MacLeod, J. Cates, A. Tannenbaum. In Proceedings of the 34th Annual International Conference of the IEEE EMBS, pp. 3211--214. 2012.
PubMed ID: 23366609
PubMed Central ID: PMC3652873
We propose an automatic approach for segmenting the left atrium from MRI images. In particular, the thoracic aorta is detected and used as a salient feature to find a seed region that lies inside the left atrium. A hybrid energy that combines robust statistics and localized region intensity information is employed to evolve active contours from the seed region to capture the whole left atrium. The experimental results demonstrate the accuracy and robustness of our approach.
Combining In-Situ and In-Transit Processing to Enable Extreme-Scale Scientific Analysis
J.C. Bennett, H. Abbasi, P. Bremer, R.W. Grout, A. Gyulassy, T. Jin, S. Klasky, H. Kolla, M. Parashar, V. Pascucci, P. Pbay, D. Thompson, H. Yu, F. Zhang, J. Chen. In ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), Salt Lake City, Utah, U.S.A., November, 2012.
With the onset of extreme-scale computing, I/O constraints make it increasingly difficult for scientists to save a sufficient amount of raw simulation data to persistent storage. One potential solution is to change the data analysis pipeline from a post-process centric to a concurrent approach based on either in-situ or in-transit processing. In this context computations are considered in-situ if they utilize the primary compute resources, while in-transit processing refers to offloading computations to a set of secondary resources using asynchronous data transfers. In this paper we explore the design and implementation of three common analysis techniques typically performed on large-scale scientific simulations: topological analysis, descriptive statistics, and visualization. We summarize algorithmic developments, describe a resource scheduling system to coordinate the execution of various analysis workflows, and discuss our implementation using the DataSpaces and ADIOS frameworks that support efficient data movement between in-situ and in-transit computations. We demonstrate the efficiency of our lightweight, flexible framework by deploying it on the Jaguar XK6 to analyze data generated by S3D, a massively parallel turbulent combustion code. Our framework allows scientists dealing with the data deluge at extreme scale to perform analyses at increased temporal resolutions, mitigate I/O costs, and significantly improve the time to insight.
Fingerprint Image Segmentation using Data Manifold Characteristic Features
A.R.C. Paiva, T. Tasdizen. In International Journal of Pattern Recognition and Artificial Intelligence, Vol. 26, No. 4, pp. (23 pages). 2012.
Automatic fingerprint identification systems (AFIS) have been studied extensively and are widely used for biometric identification. Given its importance, many well-engineered methods have been developed for the different stages that encompass those systems. The first stage of any such system is the segmentation of the actual fingerprint region from the background. This is typically achieved by classifying pixels, or blocks of pixels, based on a set of features. In this paper, we describe novel features for fingerprint segmentation that express the underlying manifold topology associated with image patches in a local neighborhood. It is shown that fingerprint patches seen in a high-dimensional space form a simple and highly regular circular manifold. The characterization of the manifold topology suggests a set of optimal features that characterize the local properties of the fingerprint. Thus, fingerprint segmentation can be formulated as a classification problem based on the deviation from the expected topology. This leads to features that are more robust to changes in contrast than mean, variance and coherence. The superior performance of the proposed features for fingerprint segmentation is shown in the eight datasets from the 2002 and 2004 Fingerprint Verification Competitions.
Keywords: Fingerprint segmentation, manifold characterization, feature extraction, dimensionality reduction
Edge Enhanced Spatio-Temporal Constrained Reconstruction of Undersampled Dynamic Contrast Enhanced Radial MRI
S.K. Iyer, T. Tasdizen, E.V.R. DiBella. In Magnetic Resonance Imaging, Vol. 30, No. 5, pp. 610--619. 2012.
Dynamic contrast-enhanced magnetic resonance imaging (MRI) is a technique used to study and track contrast kinetics in an area of interest in the body over time. Reconstruction of images with high contrast and sharp edges from undersampled data is a challenge. While good results have been reported using a radial acquisition and a spatiotemporal constrained reconstruction (STCR) method, we propose improvements from using spatially adaptive weighting and an additional edge-based constraint. The new method uses intensity gradients from a sliding window reference image to improve the sharpness of edges in the reconstructed image. The method was tested on eight radial cardiac perfusion data sets with 24 rays and compared to the STCR method. The reconstructions showed that the new method, termed edge-enhanced spatiotemporal constrained reconstruction, was able to reconstruct images with sharper edges, and there were a 36\%±13.7\% increase in contrast-to-noise ratio and a 24\%±11\% increase in contrast near the edges when compared to STCR. The novelty of this paper is the combination of spatially adaptive weighting for spatial total variation (TV) constraint along with a gradient matching term to improve the sharpness of edges. The edge map from a reference image allows the reconstruction to trade-off between TV and edge enhancement, depending on the spatially varying weighting provided by the edge map.
Keywords: MRI, Reconstruction, Edge enhanced, Compressed sensing, Regularization, Cardiac perfusion
Segmentation of Haematopoeitic Cells in Bone Marrow Using Circle Detection and Splitting Techniques
N. Ramesh, M.E. Salama, T. Tasdizen. In 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 206--209. 2012.
Bone marrow evaluation is indicated when peripheral blood abnormalities are not explained by clinical, physical, or laboratory findings. In this paper, we propose a novel method for segmentation of haematopoietic cells in the bone marrow from scanned slide images. Segmentation of clumped cells is a challenging problem for this application. We first use color information and morphology to eliminate red blood cells and the background. Clumped haematopoietic cells are then segmented using circle detection and a splitting algorithm based on the detected circle centers. The Hough Transform is used for circle detection and to find the number and positions of circle centers in each region. The splitting algorithm is based on detecting the maximum curvature points, and partitioning them based on information obtained from the centers of the circles in each region. The performance of the segmentation algorithm for haematopoietic cells is evaluated by comparing our proposed method with a hematologist's visual segmentation in a set of 3748 cells.
Serial section registration of axonal confocal microscopy datasets for long-range neural circuit reconstruction
L. Hogrebe, A.R.C. Paiva, E. Jurrus, C. Christensen, M. Bridge, L. Dai, R.L. Pfeiffer, P.R. Hof, B. Roysam, J.R. Korenberg, T. Tasdizen. In Journal of Neuroscience Methods, Vol. 207, No. 2, pp. 200--210. 2012.
In the context of long-range digital neural circuit reconstruction, this paper investigates an approach for registering axons across histological serial sections. Tracing distinctly labeled axons over large distances allows neuroscientists to study very explicit relationships between the brain's complex interconnects and, for example, diseases or aberrant development. Large scale histological analysis requires, however, that the tissue be cut into sections. In immunohistochemical studies thin sections are easily distorted due to the cutting, preparation, and slide mounting processes. In this work we target the registration of thin serial sections containing axons. Sections are first traced to extract axon centerlines, and these traces are used to define registration landmarks where they intersect section boundaries. The trace data also provides distinguishing information regarding an axon's size and orientation within a section. We propose the use of these features when pairing axons across sections in addition to utilizing the spatial relationships among the landmarks. The global rotation and translation of an unregistered section are accounted for using a random sample consensus (RANSAC) based technique. An iterative nonrigid refinement process using B-spline warping is then used to reconnect axons and produce the sought after connectivity information.
Segmentation and Two-Step Classification of White Blood Cells in Peripheral Blood Smear
N. Ramesh, B. J. Dangott, M. Salama, T. Tasdizen. In Journal of Pathology Informatics, Vol. 3, No. 13, 2012.
An automated system for differential white blood cell (WBC) counting based on morphology can make manual differential leukocyte counts faster and less tedious for pathologists and laboratory professionals. We present an automated system for isolation and classification of WBCs in manually prepared, Wright stained, peripheral blood smears from whole slide images (WSI). Methods: A simple, classification scheme using color information and morphology is proposed. The performance of the algorithm was evaluated by comparing our proposed method with a hematopathologist's visual classification. The isolation algorithm was applied to 1938 subimages of WBCs, 1804 of them were accurately isolated. Then, as the first step of a two-step classification process, WBCs were broadly classified into cells with segmented nuclei and cells with nonsegmented nuclei. The nucleus shape is one of the key factors in deciding how to classify WBCs. Ambiguities associated with connected nuclear lobes are resolved by detecting maximum curvature points and partitioning them using geometric rules. The second step is to define a set of features using the information from the cytoplasm and nuclear regions to classify WBCs using linear discriminant analysis. This two-step classification approach stratifies normal WBC types accurately from a whole slide image. Results: System evaluation is performed using a 10-fold cross-validation technique. Confusion matrix of the classifier is presented to evaluate the accuracy for each type of WBC detection. Experiments show that the two-step classification implemented achieves a 93.9\% overall accuracy in the five subtype classification. Conclusion: Our methodology achieves a semiautomated system for the detection and classification of normal WBCs from scanned WSI. Further studies will be focused on detecting and segmenting abnormal WBCs, comparison of 20x and 40x data, and expanding the applications for bone marrow aspirates.
Edge enhanced spatio-temporal constrained reconstruction of undersampled dynamic contrast enhanced radial MRI
E.V.R. DiBella, T. Tasdizen. In Proceedings of the 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 704--707. 2012.
There are many applications in MRI where it is desirable to have high spatial and high temporal resolution. This can be achieved by undersampling of k-space and requires special techniques for reconstruction. Even if undersampling artifacts are removed, sharpness of the edges can be a problem. We propose a new technique that uses the gradient from a reference image to improve the quality of the edges in the reconstructed image along with a spatio-temporal constraint to reduce aliasing artifacts and noise. The reference is created from undersampled dynamic data by combining several adjacent frames. The method was tested on undersampled radial DCE MRI data with little respiratory motion. The proposed method was compared to reconstruction using the spatio-temporal constrained reconstruction. Sharper edges and an increase in the contrast was observed by using the proposed method.
Differences in subcortical structures in young adolescents at familial risk for schizophrenia: A preliminary study
M.K. Dougherty, H. Gu, J. Bizzell, S. Ramsey, G. Gerig, D.O. Perkins, A. Belger. In Psychiatry Res., pp. (Epub ahead of print. Nov. 9, 2012.
PubMed ID: 23146250
How Many Templates Does It Take for a Good Segmentation?: Error Analysis in Multiatlas Segmentation as a Function of Database Size
S.P. Awate, P. Zhu, R.T. Whitaker. 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.
Watershed Merge Tree Classification for Electron Microscopy Image Segmentation
T. Liu, E. Jurrus, M. Seyedhosseini, M. Ellisman, T. Tasdizen. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR), pp. 133--137. 2012.
Automated segmentation of electron microscopy (EM) images is a challenging problem. In this paper, we present a novel method that utilizes a hierarchical structure and boundary classification for 2D neuron segmentation. With a membrane detection probability map, a watershed merge tree is built for the representation of hierarchical region merging from the watershed algorithm. A boundary classifier is learned with non-local image features to predict each potential merge in the tree, upon which merge decisions are made with consistency constraints to acquire the final segmentation. Independent of classifiers and decision strategies, our approach proposes a general framework for efficient hierarchical segmentation with statistical learning. We demonstrate that our method leads to a substantial improvement in segmentation accuracy.
Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy
M. Datar, P. Muralidharan, A. Kumar, S. Gouttard, J. Piven, G. Gerig, R.T. Whitaker, P.T. Fletcher. 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
Topology Preserving Atlas Construction from Shape Data without Correspondence using Sparse Parameters
S. Durrleman, M.W. Prastawa, S. Joshi, G. Gerig, A. Trouve. In Proceedings of MICCAI 2012, Lecture Notes in Computer Science (LNCS), pp. 223--230. October, 2012.
Statistical analysis of shapes, performed by constructing an atlas composed of an average model of shapes within a population and associated deformation maps, is a fundamental aspect of medical imaging studies. Usual methods for constructing a shape atlas require point correspondences across subjects, which are difficult in practice. By contrast, methods based on currents do not require correspondence. However, existing atlas construction methods using currents suffer from two limitations. First, the template current is not in the form of a topologically correct mesh, which makes direct analysis on shapes difficult. Second, the deformations are parametrized by vectors at the same location as the normals of the template current which often provides a parametrization that is more dense than required. In this paper, we propose a novel method for constructing shape atlases using currents where topology of the template is preserved and deformation parameters are optimized independently of the shape parameters. We use an L1-type prior that enables us to adaptively compute sparse and low dimensional parameterization of deformations.We show an application of our method for comparing anatomical shapes of patients with Down’s syndrome and healthy controls, where the sparse parametrization of diffeomorphisms decreases the parameter dimension by one order of magnitude.
Analysis of Longitudinal Shape Variability via Subject Specific Growth Modeling
J. Fishbaugh, M.W. Prastawa, S. Durrleman, G. Gerig. In Medical Image Computing and Computer-Assisted Intervention – Proceedings of MICCAI 2012, Lecture Notes in Computer Science (LNCS), Vol. 7510, pp. 731--738. October, 2012.
Statistical analysis of longitudinal imaging data is crucial for understanding normal anatomical development as well as disease progression. This fundamental task is challenging due to the difficulty in modeling longitudinal changes, such as growth, and comparing changes across different populations. We propose a new approach for analyzing shape variability over time, and for quantifying spatiotemporal population differences. Our approach estimates 4D anatomical growth models for a reference population (an average model) and for individuals in different groups. We define a reference 4D space for our analysis as the average population model and measure shape variability through diffeomorphisms that map the reference to the individuals. Conducting our analysis on this 4D space enables straightforward statistical analysis of deformations as they are parameterized by momenta vectors that are located at homologous locations in space and time. We evaluate our method on a synthetic shape database and clinical data from a study that seeks to quantify growth differences in subjects at risk for autism.
Neuroimaging of Structural Pathology and Connectomics in Traumatic Brain Injury: Toward Personalized Outcome Prediction
A. Irimia, Bo Wang, S.R. Aylward, M.W. Prastawa, D.F. Pace, G. Gerig, D.A. Hovda, R.Kikinis, P.M. Vespa, J.D. Van Horn. In NeuroImage: Clinical, Vol. 1, No. 1, Elsvier, pp. 1--17. 2012.
Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI]related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the communityfs attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome.
Fractional Anisotropy Distributions in 2-6 Year-Old Children with Autism
C. Cascio, M.J. Gribbin, S. Gouttard, R.G. Smith, M. Jomier, S.H. Field, M. Graves, H.C. Hazlett, K. Muller, G. Gerig, J. Piven. In Journal of Intellectual Disability Research (JIDR), pp. (in print). 2012.
Background: Increasing evidence suggests that autism is a disorder of distributed neural networks that may exhibit abnormal developmental trajectories. Characterization of white matter early in the developmental course of the disorder is critical to understanding these aberrant trajectories.
Methods: A cross-sectional study of 2-6 year old children with autism was conducted using diffusion tensor imaging combined with a novel statistical approach employing fractional anisotropy distributions. 58 children aged 18-79 months were imaged: 33 were diagnosed with autism, 8 with general developmental delay (DD), and 17 were typically developing (TD). Fractional anisotropy values within global white matter, cortical lobes, and the cerebellum were measured and transformed to random F distributions for each subject. Each distribution of values for a region was summarized by estimating delta, the estimated mean and standard deviation of the approximating F for each distribution.
Results: The estimated delta parameter, delta-hat, was significantly decreased in individuals with autism compared to the combined control group. This was true in all cortical lobes, as well as in the cerebellum, but differences were strongest in the temporal lobe. Predicted developmental trajectories of delta-hat across the age range in the sample showed patterns that partially distinguished the groups. Exploratory analyses suggested that the variability, rather than the central tendency, component of delta-hat was the driving force behind these results. Conclusions: White matter in young children with autism appears to be abnormally homogeneous, which may reflect poorly organized or differentiated pathways, particularly in the temporal lobe, which is important for social and emotional cognition.
White matter structure assessment from reduced HARDI data using low-rank polynomial approximations
Y. Gur, F. Jiao, S.X. Zhu, C.R. Johnson. 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.
Sasaki Metrics for Analysis of Longitudinal Data on Manifolds
P. Muralidharan, P.T. Fletcher. In Proceedings of the 2012 IEEE conference on Computer Vision and Pattern Recognition (CVPR), pp. 1027--1034. 2012.
Longitudinal data arises in many applications in which the goal is to understand changes in individual entities over time. In this paper, we present a method for analyzing longitudinal data that take values in a Riemannian manifold. A driving application is to characterize anatomical shape changes and to distinguish between trends in anatomy that are healthy versus those that are due to disease. We present a generative hierarchical model in which each individual is modeled by a geodesic trend, which in turn is considered as a perturbation of the mean geodesic trend for the population. Each geodesic in the model can be uniquely parameterized by a starting point and velocity, i.e., a point in the tangent bundle. Comparison between these parameters is achieved through the Sasaki metric, which provides a natural distance metric on the tangent bundle. We develop a statistical hypothesis test for differences between two groups of longitudinal data by generalizing the Hotelling T2 statistic to manifolds. We demonstrate the ability of these methods to distinguish differences in shape changes in a comparison of longitudinal corpus callosum data in subjects with dementia versus healthily aging controls.
A System for Query Based Analysis and Visualization
A.R. Sanderson, B. Whitlock, O. Reubel, H. Childs, G.H. Weber, Prabhat, K. Wu. In Proceedings of the Third International Eurovis Workshop on Visual Analytics EuroVA 2012, pp. 25--29. June, 2012.
Today scientists are producing large volumes of data that they wish to explore and visualize. In this paper we describe a system that combines range-based queries with fast lookup to allow a scientist to quickly and efficiently ask \"what if?\" questions. Unique to our system is the ability to perform "cumulative queries" that work on both an intra- and inter-time step basis. The results of such queries are visualized as frequency histograms and are the input for secondary queries, the results of which are then visualized.
Semi-Automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron Microscopy Images
E. Jurrus, S. Watanabe, R.J. Giuly, A.R.C. Paiva, M.H. Ellisman, E.M. Jorgensen, T. Tasdizen. In Neuroinformatics, pp. (published online). 2012.
Neuroscientists are developing new imaging techniques and generating large volumes of data in an effort to understand the complex structure of the nervous system. The complexity and size of this data makes human interpretation a labor-intensive task. To aid in the analysis, new segmentation techniques for identifying neurons in these feature rich datasets are required. This paper presents a method for neuron boundary detection and nonbranching process segmentation in electron microscopy images and visualizing them in three dimensions. It combines both automated segmentation techniques with a graphical user interface for correction of mistakes in the automated process. The automated process first uses machine learning and image processing techniques to identify neuron membranes that deliniate the cells in each two-dimensional section. To segment nonbranching processes, the cell regions in each two-dimensional section are connected in 3D using correlation of regions between sections. The combination of this method with a graphical user interface specially designed for this purpose, enables users to quickly segment cellular processes in large volumes.