Ross WhitakerSegmentation |
Sarang JoshiShape StatisticsSegmentation Brain Atlasing |
Tolga TasdizenImage ProcessingMachine Learning |
Tom FletcherShape StatisticsDiffusion Tensor Analysis |
Chris JohnsonDiffusion Tensor Analysis |
Automatic classification of scar tissue in late gadolinium enhancement cardiac MRI for the assessment of left-atrial wall injury after radiofrequency ablation D. Perry, A. Morris, N. Burgon, C. McGann, R.S. MacLeod, J. Cates. In SPIE Proceedings, Vol. 8315, pp. (published online). 2012. DOI: 10.1117/12.910833 PubMed ID: 24236224 PubMed Central ID: PMC3824273 Radiofrequency ablation is a promising procedure for treating atrial fibrillation (AF) that relies on accurate lesion delivery in the left atrial (LA) wall for success. Late Gadolinium Enhancement MRI (LGE MRI) at three months post-ablation has proven effective for noninvasive assessment of the location and extent of scar formation, which are important factors for predicting patient outcome and planning of redo ablation procedures. We have developed an algorithm for automatic classification in LGE MRI of scar tissue in the LA wall and have evaluated accuracy and consistency compared to manual scar classifications by expert observers. Our approach clusters voxels based on normalized intensity and was chosen through a systematic comparison of the performance of multivariate clustering on many combinations of image texture. Algorithm performance was determined by overlap with ground truth, using multiple overlap measures, and the accuracy of the estimation of the total amount of scar in the LA. Ground truth was determined using the STAPLE algorithm, which produces a probabilistic estimate of the true scar classification from multiple expert manual segmentations. Evaluation of the ground truth data set was based on both inter- and intra-observer agreement, with variation among expert classifiers indicating the difficulty of scar classification for a given a dataset. Our proposed automatic scar classification algorithm performs well for both scar localization and estimation of scar volume: for ground truth datasets considered easy, variability from the ground truth was low; for those considered difficult, variability from ground truth was on par with the variability across experts. |
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. DOI: 10.1109/EMBC.2012.6346648 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. |
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. DOI: 10.1142/S0218001412560101 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 |
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. DOI: 10.1109/ISBI.2012.6235520 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. DOI: 10.1016/j.jneumeth.2012.03.002 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. |
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. DOI: 10.1109/ISBI.2010.5490077 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. DOI: 10.1016/j.pscychresns.2012.04.016 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. |
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. ISBN: 978-3-642-33554-9 DOI: 10.1007/978-3-642-33555-6_7 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 T^{2} 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 |
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. DOI: 10.1007/978-3-642-33415-3_90 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. DOI: 10.1016/j.nicl.2012.08.002 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. |
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. DOI: 10.1109/CVPR.2012.6247780 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 T^{2} 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. |