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Data driven brain tumor segmentation in MRI using probabilistic reasoning over space and time

medical image computing and computer-assisted intervention, MICCAI 2004 - 7th international conference, Proceedings

作者： Solomon, Jeffrey Butman, John A. Sood, Arun Center for Image Analysis George Mason University United States Diagnostic Radiology Department Clinical Center National Institutes of Health United States Medical Numerics Inc. United States

ISBN: (纸本)3540229760

A semi-automated method for brain tumor segmentation and volume tracking has been developed. This method uses a pipeline approach to process MRI images. The pipeline process involves the following steps: 1) automatic alignment of initial and subsequent MRI scans for a given patient, 2) automatic de-skulling of all brain images, 3) automatic segmentation of the brain tumors using probabilistic reasoning over space and time with a semi-automatic correction of segmentation results on the first time point only and, 4) brain tumor tracking, providing a report of tumor volume change. To validate the procedure, we evaluated contrast enhanced MRI images from five brain tumor patients, each scanned at three times, several months apart. This data was processed and estimated tumor volume results show good agreement with manual tracing of 3D lesions over time. © Springer-Verlag Berlin Heidelberg 2004.

关键词： image segmentation

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Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm

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medical image ANALYSIS 2004年第3期8卷 255-265页

作者： Lorenzo-Valdés, M Sanchez-Ortiz, GI Elkington, AG Mohiaddin, RH Rueckert, D Univ London Imperial Coll Sci Technol & Med Dept Comp Visual Informat Proc Grp London SW7 2BZ England Royal Brompton & Harfield NHS Trust Cardiovasc Magnet Resonance Unit London SW3 6NP England

In this paper an automatic atlas-based segmentation algorithm for 4D cardiac MR images is proposed. The algorithm is based on the 4D extension of the expectation maximisation (EM) algorithm. The EM algorithm uses a 4D probabilistic cardiac atlas to estimate the initial model parameters and to integrate a priori information into the classification process. The probabilistic cardiac atlas has been constructed from the manual segmentations of 3D cardiac image sequences of 14 healthy volunteers. It provides space and time-varying probability maps for the left and right ventricles, the myocardium, and background structures such as the liver, stomach, lungs and skin. In addition to using the probabilistic cardiac atlas as a priori information, the segmentation algorithm incorporates spatial and temporal contextual information by using 4D Markov Random Fields. After the classification, the largest connected component of each structure is extracted using a global connectivity filter which improves the results significantly, especially for the myocardium. Validation against manual segmentations and computation of the correlation between manual and automatic segmentation on 249 3D volumes were calculated. We used the 'leave one out' test where the image set to be segmented was not used in the construction of its corresponding atlas. Results show that the procedure can successfully segment the left ventricle (LV) (r = 0.96), myocardium (r = 0.92) and right ventricle (r = 0.92). In addition, 4D images from 10 patients with hypertrophic cardiornyopathy were also manually and automatically segmented yielding a good correlation in the volumes of the LV (r = 0.93) and myocardium (0.94) when the atlas constructed with volunteers is blurred. (C) 2004 Elsevier B.V. All rights reserved.

关键词： classification Markov random fields heart cardiac magnetic resonance largest connected component time sequences

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Automatic optimization of segmentation algorithms through simultaneous truth and performance level estimation (STAPLE)

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medical image computing and computer-assisted intervention, MICCAI 2004 - 7th international conference, Proceedings

作者： Maddah, Mahnaz Zou, Kelly H. Wells, William M. Kikinis, Ron Warfield, Simon K. Computational Radiology Laboratory Brigham and Women's Hospital Harvard Medical School Boston MA 02115 United States

ISBN: (纸本)3540229760

The performance of automatic segmentation algorithms often depends critically upon a number of parameters intrinsic to the algorithm. Appropriate setting of these parameters is a pre-requisite for successful segmentation, and yet may be difficult for users to achieve. We propose here a novel algorithm for the automatic selection of optimal parameters for medical image segmentation. Our algorithm makes use of STAPLE (Simultaneous Truth and Performance Level Estimation), a previously described and validated algorithm for automatically identifying a reference standard by which to assess segmentation generators. We execute a set of independent automated segmentation algorithms with initial parameter settings, on a set of images from any clinical application under consideration, estimate a reference standard from the segmentation results using STAPLE, and then identify the parameter settings for each algorithm that maximizes the quality of the segmentation generator result with respect to the reference standard. The process of estimating a reference standard and estimating the optimal parameter settings is iterated to convergence. © Springer-Verlag Berlin Heidelberg 2004.

关键词： image segmentation

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A framework for detailed objective comparison of non-rigid registration algorithms in neuroimaging

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medical image computing and computer-assisted intervention, MICCAI 2004 - 7th international conference, Proceedings

作者： Crum, William R. Rueckert, Daniel Jenkinson, Mark Kennedy, David Smith, Stephen M. Division of Imaging Sciences Thomas Guy House Guy's Hospital London SEI 9RT United Kingdom Department of Computing Imperial College London SW7 2AZ United Kingdom Oxford Centre for Functional Magnetic Resonance Imaging of the Brain John Radcliffe Hospital Headington Oxford OX3 9DU United Kingdom Center for Morphometric Analysis Department of Neurology MGH 13th Street Charlestown MA 02129 United States

ISBN: (纸本)3540229760

Non-rigid image registration is widely used in the analysis of brain images to the extent it is provided as a standard tool in common packages such as SPM. However the performance of algorithms in specific applications remains hard to measure. In this paper a detailed comparison of the performance of an affine, B-Spline control-point and viscous fluid registration algorithm in inter-subject brain registration is presented. The comparison makes use of highly detailed expert manual labellings of a range of structures distributed in scale and in location in the brain. The overall performance is B-Spline, fluid, affine (best first) with all algorithms struggling to improve the match of smaller structures. We discuss caveats, evaluation strategies for registration and implications for future registration-based neuroimaging studies. © Springer-Verlag Berlin Heidelberg 2004.

关键词： medical imaging

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3D bayesian regularization of diffusion tensor MRI using multivariate gaussian markov random fields

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medical image computing and computer-assisted intervention, MICCAI 2004 - 7th international conference, Proceedings

作者： Martín-Fernández, Marcos Westin, Carl-Fredrik Alberola-López, Carlos Image Processing Lab. University of Valladolid 47011 Valladolid Spain Lab. of Mathematics in Imaging Brighman and Women's Hospital Harvard Medical School Boston MA 02115 United States

ISBN: (纸本)3540229760

3D Bayesian regularization applied to diffusion tensor MRI is presented here. The approach uses Markov Random Field ideas and is based upon the definition of a 3D neighborhood system in which the spatial interactions of the tensors are modeled. As for the prior, we model the behavior of the tensor fields by means of a 6D multivariate Gaussian local characteristic. As for the likelihood, we model the noise process by means of conditionally independent 6D multivariate Gaussian variables. Those models include inter-tensor correlations, intra-tensor correlations and colored noise. The solution tensor field is obtained by using the simulated annealing algorithm to achieve the maximum a posteriori estimation. Several experiments both on synthetic and real data are presented, and performance is assessed with mean square error measure. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Magnetic resonance imaging

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Construction of a 3D volumetric probabilistic model of the mouse kidney from MRI

Construction of a 3D volumetric probabilistic model of the m...

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medical image computing and computer-assisted intervention, MICCAI 2004 - 7th international conference, Proceedings

作者： Okuda, Hirohito Shkarin, Pavel Behar, Kevin Duncan, James S. Papademetris, Xenophon Production Engineering and Research Lab. Hitachi Ltd. Kanagawa Japan Department of Biomed. Engineering Yale University New Haven CT 06520-8042 United States Department of Diag. Radiology Yale University New Haven CT 06520-8042 United States Department of Phsychiatry Yale University New Haven CT 06520-8042 United States

ISBN: (纸本)3540229779

We present the results of constructing a probabilistic volumetric model of 3D MR kidney images. The ultimate goal of this work is the mouse kidney segmentation based on a probabilistic volumetric model. The kidneys were aligned into the base shape using an extended robust point matching algorithm. The registration step consists of the global linear transformation and the local B-spline based free form deformation. Shape modeling is performed with globally aligned shape and template volumetric image is generated with locally aligned images. We are currently working on developing a segmentation algorithm using our model. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Physiological models

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A data clustering and streamline reduction method for 3D MR flow vector field simplification

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medical image computing and computer-assisted intervention, MICCAI 2004 - 7th international conference, Proceedings

作者： Carmo, Bernardo S. Ng, Y. H. Pauline Prugei-Bennett, Adam Yang, Guang-Zhong Department of Electronics and Computer Science University of Southampton SO17 1BJ United Kingdom Department of Computing Imperial College London 180 Queen's Gate London SW7 2BZ United Kingdom

ISBN: (纸本)3540229760

With the increasing capability of MR imaging and Computational Fluid Dynamics (CFD) techniques, a significant amount of data related to the haemodynamics of the cardiovascular systems are being generated. Direct visualization of the data introduces unnecessary visual clutter and hides away the underlying trend associated with the progression of the disease. To elucidate the main topological structure of the flow fields, we present in this paper a 3D visualisation method based on the abstraction of complex flow fields. It uses hierarchical clustering and local linear expansion to extract salient topological flow features. This is then combined with 3D streamline tracking, allowing most important flow details to be visualized. Example results of the technique applied to both CFD and in vivo MR data sets are provided. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Magnetic resonance imaging

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3D/4D cardiac segmentation using active appearance models, non-rigid registration, and the Insight Toolkit

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medical image computing and computer-assisted intervention, MICCAI 2004 - 7th international conference, Proceedings

作者： Lapp, Robert M. Lorenzo-Valdés, Maria Rueckert, Daniel Institute of Medical Physics University of Erlangen-Nürnberg Henkestrasse 91 91052 Erlangen Germany Visual Information Processing Group Department of Computing Imperial College London 180 Queen's Gate London SW7 2BZ United Kingdom

ISBN: (纸本)3540229760

We describe the design of a statistical atlas-based 3D/4D cardiac segmentation system using a combination of active appearance models (AAM) and statistical deformation models with the Insight Toolkit as an underlying implementation framework. Since the original AAM approach was developed for 2D applications and makes use of manually set landmarks its extension to higher dimensional data sets cannot be easily achieved. We therefore apply the idea of statistical deformation models to AAMs and use a deformable registration step for establishing point-to-point correspondences. An evaluation of the implemented system was performed by segmenting the left ventricle cavity, myocardium and right ventricle of ten cardiac MRI and ten CT datasets. The comparison of automatic and manual segmentations showed encouraging results with a mean segmentation error of 2.2±1.1 mm. We conclude that the combination of a non-rigid registration step with the statistical analysis concepts of the AAM is both feasible and useful and allows for its application to 3D and 4D data. © Springer-Verlag Berlin Heidelberg 2004.

关键词： image segmentation

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3D analysis of radiofrequency-ablated tumors in liver: A computer-aided diagnosis tool for early detection of local recurrences

3D analysis of radiofrequency-ablated tumors in liver: A com...

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medical image computing and computer-assisted intervention, MICCAI 2004 - 7th international conference, Proceedings

作者： Bricault, Ivan Kikinis, Ron Vansonnenberg, Eric Tuncali, Kemal Silverman, Stuart G. Surgical Planning Lab. Department of Radiology Brigham and Women's Hospital 75 Francis Street Boston MA 02115 United States TIMC-IMAG Laboratory University Hospital Grenoble France

ISBN: (纸本)3540229779

Radiofrequency ablation is being used increasingly in the treatment of liver tumors, and the detection of local recurrences on follow-up imaging is an important and occasionally challenging task. When the tumor is not associated with nodular enhancement, recurrence detection only relies on a precise identification of shape changes in the post-ablation area. In order to better characterize subtle shape changes, we present a computer-aided diagnosis tool based on a semi-automatic segmentation of CT data with a watershed algorithm. The 3D moment of inertia of the segmented area is computed and provides a quantitative criterion for the study of post-ablation changes over time. Preliminary results on two clinical cases demonstrate that our tool can effectively improve the radiologist's ability to detect early tumor recurrence. © Springer-Verlag Berlin Heidelberg 2004.

关键词： computerized tomography

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Texture image analysis for osteoporosis detection with morphological tools

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medical image computing and computer-assisted intervention, MICCAI 2004 - 7th international conference, Proceedings

作者： Sevestre-Ghalila, Sylvie Benazza-Benyahia, Amel Ricordeau, Anne Mellouli, Nedra Chappard, Christine Benhamou, Claude Laurent Université Paris 5 Laboratoire MAP5 45 rue des Saints Pères 75270 Paris Cédex 06 France Université Manouba ENSI Campus de la Manouba 2020 La Manouba Tunisia Ecole Supérieure des Communications de Tunis URISA Tunisia Université Paris 8 LINC IUT de Montreuil Montreuil France CHR d'Orléans France

ISBN: (纸本)3540229760

Osteoporosis is due to the following two phenomena: a reduction bone mass and a degradation of the microarchitecture of bone tissue. In this paper, we propose a method for extracting morphological information enabling the description of bone structure from radiological images of the calcaneus. Our main contribution relies on the fact that we provide bone descriptors close to classical 3D-morphological bone parameters. The first step of the proposed method consists in extracting the grey-scale skeleton of the microstructures contained in the underlying images. After an appropriate processing, the resulting skeleton provides discriminant features between osteoporotic patients and control patients. Statistical tests corroborate this discrimination property. © Springer-Verlag Berlin Heidelberg 2004.

关键词： medical imaging

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