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

作者： Chan, C. Barratt, D.C. Edwards1, P.J. Penney, G.P. Slomczykowski, M. Carter, T.J. Hawkes, D.J. Imaging Sciences Division King’s College London Guy’s Hospital London United Kingdom Department of Surgical Oncology and Technology Imperial College London London United Kingdom i-Orthopaedics DePuy a Johnson and Johnson company Heimstetten Germany

ISBN: (纸本)3540229779

We present cadaver validation of a method that uses tracked ultrasound to instantiate and register 3D statistical shape models (SSMs) of 3 femurs and 2 pelves. the SSMs were generated directly from the deformation fields obtained from non-rigid registration of CT images to a single target CT image. Ultrasound images were obtained from three intact cadavers. these were used to instantiate the model by iteratively minimising the RMS distance between the model surface and the ultrasound-derived bone surface points. the RMS distance between the instantiated model surface and the CT-derived bone surface was less than 3.72mm in the region of the femoral head and acetabulum. We conclude that SSMs of the femur and pelvis may be instantiated and registered to surgical space to within a clinically acceptable accuracy using intraoperative ultrasound. this potentially could reduce the invasiveness of orthopaedic procedures, and remove the requirement for a preoperative CT scan. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Ultrasonics

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Determination of aortic distensibility using non-rigid registration of cine MR images

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7th international conference on medical Imaging and computer assisted intervention, MICCAI 2004

作者： Lorenzo-Valdés, Maria Sanchez-Ortiz, Gerardo I. Bogren, Hugo Mohiaddin, Raad Rueckert, Daniel Visual Information Processing Group Department of Computing Imperial College London 180 Queen’s Gate LondonSW7 2BZ United Kingdom Royal Brompton and Harefield NHS Trust Sydney Street London United Kingdom

ISBN: (纸本)3540229760

A novel method for the estimation of areas in 2D MR images of the aorta is presented. the method uses spatio-temporal non-rigid registration in order to obtain the 2D deformation fields of the vessels during the cardiac cycle. this is accomplished by aligning all time frames in the image sequence simultaneously to the first one. the determinant of the Jacobian of the 2D deformation fields are then computed to obtain the expansion (or contraction) at each time frame, with respect to the first time frame. By using 3D splines, the method exploits the relation between time frames in order to obtain continuous and smooth distensibility measurements throughout the cardiac cycle. Validation was carried out with MR images of the aorta. Experiments for the registration and estimation of areas in the aorta are presented in 60 data sets corresponding to three different sections of the aorta (proximal, mid and distal) in 20 different subjects, where each set consisted of 17 to 38 time frames. Manually estimated areas are compared to the areas estimated automatically in 8 data sets where the average error is 2.3% of the area manually obtained. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Deformation

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Functional brain image analysis using joint function-structure priors

Functional brain image analysis using joint function-structu...

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

作者： Yang, Jing Papademetris, Xenophon Staib, Lawrence H. Schultz, Robert T. Duncan, James S. Departments of Electrical Engineering Yale University P.O. Box 208042 New Haven CT 06520-8042 United States Diagnostic Radiology Yale University P.O. Box 208042 New Haven CT 06520-8042 United States Child Study Center Yale University P.O. Box 208042 New Haven CT 06520-8042 United States

ISBN: (纸本)3540229779

We propose a new method for context-driven analysis of functional magnetic resonance images (fMRI) that incorporates spatial relationships between functional parameter clusters and anatomical structure directly for the first time. We design a parametric scheme that relates functional and structural spatially-compact regions in a single unified manner. Our method is motivated by the fact that the fMRI and anatomical MRI (aMRI) have consistent relations that provide configurations and context that aid in fMRI analysis. We develop a statistical decision-making strategy to estimate new fMRI parameter images (based on a General Linear Model-GLM) and spatially-clustered zones within these images. the analysis is based on the time-series data and contextual information related to appropriate spatial grouping of parameters in the functional data and the relationship of this grouping to relevant gray matter structure from the anatomical data. We introduce a representation for the joint prior of the functional and structural information, and define a joint probability distribution over the variations of functional clusters and the related structure contained in a set of training images. We estimate the Maximum A Posteriori (MAP) functional parameters, formulating the function-structure model in terms of level set functions. Results from 3D fMRI and aMRI show that this context-driven analysis potentially extracts more meaningful information than the standard GLM approach. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Magnetic resonance imaging

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Deformable biomechanical models: Application to 4D cardiac image analysis

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medical image ANALYSIS 2003年第4期7卷 475-488页

作者： Sermesant, M Forest, C Pennec, X Delingette, H Ayache, N INRIA Sophia Antipolis Epidaure Project Stamford CT 06902 USA

this article describes a methodology for creating a generic volumetric biomechanical model from different image modalities and segmenting time series of medical images using this model. the construction of such a generic model consists of three stages: geometric meshing. non-rigid deformation of the mesh in images of various modalities, and image-to-mesh information mapping through rasterization. the non-rigid deformation stage, which relies on a combination of global and local deformations, can then be used to segment time series of images. e.g. cine MRI or gated SPECT cardiac images. We believe that this type of deformable biomechanical model can play an important role in the extraction of useful quantitative local parameters of cardiac function. the biomechanical model of the heart will be coupled with an electrical model of another collaborative project in order to simulate and analyze a larger class of pathologies. (C) 2003 Elsevier B.V. All rights reserved.

关键词： biomechanical model tetrahedral mesh finite element method deformable model image analysis cardiac motion

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Accuracy of fully automatic vs. manual planning of cardiac MR acquisitions 1

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6th international conference on medical Imaging computing and computer-assisted intervention

作者： Danilouchkine, MG Westenberg, JJM Lamb, HJ Reiber, JHC Lelieveldt, BPF Leiden Univ Ctr Med Dept Radiol Div Image Proc NL-2300 RC Leiden Netherlands

In this paper, a system for fully automatic planning of short-axis (SA) cardiac MR acquisition is evaluated. Experimental results from 11 patients and 3 healthy subjects show that the difference between the automatic ... 详细信息

ISBN: (数字)9783540399032

ISBN: (纸本)3540204644

关键词： Artificial intelligence

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Geostatistical medical image registration

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6th international conference on medical image computing and computer-assisted intervention

作者： Ruiz-Alzola, J Suarez, E Alberola-Lopez, C Warfield, SK Westin, CF Univ Las Palmas Gran Canaria Med Technol Ctr Las Palmas Gran Canaria Spain Univ Valladolid ETSI Telecommun Valladolid Spain Harvard Univ Sch Med Cambridge MA 02138 USA Brigham & Womens Hosp Dept Radiol Boston MA 02115 USA

ISBN: (纸本)3540204644

We propose a novel approach to landmark-based medical image registration based on the geostatical method of Kriging prediction. Our method exploits the spatial statistical relation between two images, as estimated using general-purpose registration algorithms, in order to construct an optimum predictor of the displacement field. High accuracy is achieved by using an estimated spatial model of the displacement field directly from the image data, while practically circumventing the difficulties that prevented Kriging from being widely used in image registration.

关键词： Kriging

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A viscous fluid model for multimodal non-rigid image registration using mutual information

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medical image ANALYSIS 2003年第4期7卷 565-575页

作者： D'Agostino, E Maes, F Vandermeulen, D Suetens, P Katholieke Univ Leuven Univ Hosp Med Image Comp Radiol ESAT PSI Fac Med B-3000 Louvain Belgium Katholieke Univ Leuven Univ Hosp Med Image Comp Radiol ESAT PSI Fac Engn B-3000 Louvain Belgium

We propose a multimodal free-form registration algorithm based on maximization of mutual information. the warped image is modeled as a viscous fluid that deforms under the influence of forces derived from the gradient of the mutual information registration criterion. Parzen windowing is used to estimate the joint intensity probability of the images to be matched. the method is evaluated for non-rigid inter-subject registration of MR brain images. the accuracy of the method is verified using simulated multi-modal MR images with known ground truth deformation. the results show that the root mean square difference between the recovered and the ground truth deformation is smaller than 1 voxel. We illustrate the application of the method for atlas-based brain tissue segmentation in MR images in case of gross morphological differences between atlas and patient images. (C) 2003 Elsevier B.V. All rights reserved.

关键词： non-rigid-registration mutual information viscous fluid model

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Combinative multi-scale level set framework for echocardiographic image segmentation

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medical image ANALYSIS 2003年第4期7卷 529-537页

作者： Lin, N Yu, WC Duncan, JS Yale Univ Dept Elect Engn New Haven CT 06520 USA Yale Univ Dept Diagnost Radiol New Haven CT 06520 USA Yale Univ Dept Diagnost Radiol & Elect Engn New Haven CT 06520 USA

In the automatic se-mentation of echocardiographic images, a priori shape knowledge has been used to compensate for poor features in ultrasound images. this shape knowledge is often learned via an off-line training process, which requires tedious human effort and is highly expertise-dependent. More importantly, a learned shape template can only be used to segment a specific class of images with similar boundary shape. In this paper, we present a multi-scale level set framework for segmentation of endocardial boundaries at each frame in a multiframe echocardiographic image sequence. We point out that the intensity distribution of an ultrasound image at a very coarse scale can be approximately modeled by Gaussian. then we combine region homogeneity and edge features in a level set approach to extract boundaries automatically at this coarse scale. At finer scale levels, these coarse boundaries are used to both initialize boundary detection and serve as an external constraint to guide contour evolution. this constraint functions similar to a traditional shape prior. Experimental results validate this combinative framework. (C) 2003 Elsevier B.V. All rights reserved.

关键词： multi-scale level set framework echocardiographic image segmentation

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A statistically based surface evolution method for medical image segmentation: Presentation and validation 1

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6th international conference on medical image computing and computer-assisted intervention

作者： Pichon, E Tannenbaum, A Kikinis, R Georgia Inst Technol Atlanta GA 30332 USA Harvard Univ Sch Med Boston MA 02115 USA

ISBN: (数字)9783540399032

ISBN: (纸本)3540204644

In this paper we present a new algorithm for 3D medical image segmentation. the algorithm is fast, relatively simple to implement, and semi-automatic. It is based on minimizing a global energy defined from a learned non-parametric estimation of the statistics of the region to be segmented. Implementation details are discussed and source code is freely available as part of the 3D Slicer project. In addition, a new unified set of validation metrics is proposed. Results on artificial and real MRI images show that the algorithm performs well on large brain structures both in terms of accuracy and robustness to noise.

关键词： Magnetic resonance imaging

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Topological correction of subcortical segmentation 1

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6th international conference on medical image computing and computer-assisted intervention

作者： Ségonne, F Grimson, E Fischl, B MIT AI Lab Cambridge MA 02139 USA

ISBN: (数字)9783540399032

ISBN: (纸本)3540204644

We propose a method for automatically correcting the spherical topology of any segmentation under any digital connectivity. A multiple region growing process, concurrently acting on the foreground and the background, divides the segmentation into connected components and successive minimum cost decisions guarantee convergence to correct spherical topology. In contrast to existing procedures that suppose specific initial segmentation (full connectivity, no cavities...) and are designed for a particular task (cortical representation), no assumption is made on the initial image. Our method applied to subcortical segmentations allows us to correct the topology of fourteen non-cortical structures in less than a minute.

关键词： image segmentation

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