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Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling,” Medical Imaging, (2002)

by A F Frangi, D Rueckert, J A Schnabel, W J Niessen
Venue:IEEE Transactions on,
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Automatic construction of 3D statistical deformation models using non-rigid registration

by D. Rueckert, A. F. Frangi, J. A. Schnabel - IEEE Transactions on Medical Imaging , 2003
"... Abstract. In this paper we introduce the concept of statistical deformation models (SDM) which allow the construction of average models of the anatomy and their variability. SDMs are build by performing a statistical analysis of the deformations required to map anatomical features in one subject int ..."
Abstract - Cited by 161 (8 self) - Add to MetaCart
Abstract. In this paper we introduce the concept of statistical deformation models (SDM) which allow the construction of average models of the anatomy and their variability. SDMs are build by performing a statistical analysis of the deformations required to map anatomical features in one subject into the corresponding features in another subject. The concept of SDMs is similar to active shape models (ASM) which capture statistical information about shapes across a population but offers several new advantages: Firstly, SDMs can be constructed directly from images such as MR or CT without the need for segmentation which is usually a prerequisite for the construction of active shape models. Instead a non-rigid registration algorithm is used to compute the deformations required to establish correspondences between the reference subject and the subjects in the population class under investigation. Secondly, SDMs allow the construction of an atlas of the average anatomy as well as its variability across a population of subjects. Finally, SDMs take the 3D nature of the underlying anatomy into account by analysing dense 3D deformation fields rather than only the 2D surface shape of anatomical structures. We demonstrate the applicability of this new framework to MR images of the brain and show results for the construction of anatomical models from 25 different subjects. 1
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...irit, Bookstein et al. used deformation maps based on thin-plate splines to study the shape variability in patients with schizophrenia and normal controls [30], [31]. C. Contribution of This Paper In =-=[32]-=-, we described an automated way in which correspondences between the surfaces of different shapes are established via a nonrigid registration algorithm [33]. A similar approach has been proposed by Su...

Four-Chamber Heart Modeling and Automatic Segmentation for 3D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features

by Yefeng Zheng, Adrian Barbu, Bogdan Georgescu, Michael Scheuering, Dorin Comaniciu - IEEE TRANSACTIONS ON MEDICAL IMAGING , 2008
"... We propose an automatic four-chamber heart segmentation system for the quantitative functional analysis of the heart from cardiac computed tomography (CT) volumes. Two topics are discussed: heart modeling and automatic model fitting to an unseen volume. Heart modeling is a non-trivial task since the ..."
Abstract - Cited by 102 (43 self) - Add to MetaCart
We propose an automatic four-chamber heart segmentation system for the quantitative functional analysis of the heart from cardiac computed tomography (CT) volumes. Two topics are discussed: heart modeling and automatic model fitting to an unseen volume. Heart modeling is a non-trivial task since the heart is a complex nonrigid organ. The model must be anatomically accurate, allow manual editing, and provide sufficient information to guide automatic detection and segmentation. Unlike previous work, we explicitly represent important landmarks (such as the valves and the ventricular septum cusps) among the control points of the model. The control points can be detected reliably to guide the automatic model fitting process. Using this model, we develop an efficient and robust approach for automatic heart chamber segmentation in 3D CT volumes. We formulate the segmentation as a two-step learning problem: anatomical structure localization and boundary delineation. In both steps, we exploit the recent advances in learning discriminative models. A novel algorithm, marginal space learning (MSL), is introduced to solve the 9-dimensional similarity transformation search problem for localizing the heart chambers. After determining the pose of the heart chambers, we estimate the 3D shape through learning-based boundary delineation. The proposed method has been extensively tested on the largest dataset (with 323 volumes from 137 patients) ever reported in the literature. To the best of our knowledge, our system is the fastest with a speed of 4.0 seconds per volume (on a dual-core 3.2 GHz processor) for the automatic segmentation of all four chambers.

A review of geometric transformations for nonrigid body registration

by Mark Holden - IEEE TRANSACTIONS ON MEDICAL IMAGING , 2007
"... ..."
Abstract - Cited by 64 (0 self) - Add to MetaCart
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A unified information-theoretic approach to the correspondence problem in image registration

by Carole J. Twining, Tim Cootes, Stephen Marsl, Vladimir Petrovic, Roy Schestowitz, Chris J. Taylor - In Proceedings of the International Conference on Pattern Recognition (ICPR , 2004
"... Abstract. The non-rigid registration of a group of images shares a common feature with building a model of a group of images: a dense, consistent correspondence across the group. Image registration aims to find the correspondence, while modelling requires it. This paper presents the theoretical fram ..."
Abstract - Cited by 45 (10 self) - Add to MetaCart
Abstract. The non-rigid registration of a group of images shares a common feature with building a model of a group of images: a dense, consistent correspondence across the group. Image registration aims to find the correspondence, while modelling requires it. This paper presents the theoretical framework required to unify these two areas, providing a groupwise registration algorithm, where the inherently groupwise model of the image data becomes an integral part of the registration process. The performance of this algorithm is evaluated by extending the con-cepts of generalisability and specificity from shape models to image mod-els. This provides an independent metric for comparing registration al-gorithms of groups of images. Experimental results on MR data of brains for various pairwise and groupwise registration algorithms is presented, and demonstrates the feasibility of the combined registration/modelling framework, as well as providing quantitative evidence for the superiority of groupwise approaches to registration. 1
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...ence can be defined across a group of images by minimising, explicitly, an MDL objective function. The combination of non-rigid image registration with modelling was shown previously by Frangi et al. =-=[5]-=-, who used non-rigid registration to automatically construct 3D statistical shape models of the left and right ventricles of the heart. However, their method did require an initial manual labelling of...

Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models

by Zhuowen Tu, Katherine L. Narr, Piotr Dollár, Ivo Dinov, Paul M. Thompson, Arthur W. Toga
"... Abstract — In this paper, a hybrid discriminative/generative model for brain anatomical structure segmentation is proposed. The learning aspect of the approach is emphasized. In the discriminative appearance models, various cues such as intensity and curvatures are combined to locally capture the co ..."
Abstract - Cited by 38 (8 self) - Add to MetaCart
Abstract — In this paper, a hybrid discriminative/generative model for brain anatomical structure segmentation is proposed. The learning aspect of the approach is emphasized. In the discriminative appearance models, various cues such as intensity and curvatures are combined to locally capture the complex appearances of different anatomical structures. A probabilistic boosting tree (PBT) framework is adopted to learn multi-class discriminative models that combine hundreds of features across different scales. On the generative model side, both global and local shape models are used to capture the shape information about each anatomical structure. The parameters to combine the discriminative appearance and generative shape models are also automatically learned. Thus low-level and high-level information is learned and integrated in a hybrid model. Segmentations are obtained by minimizing an energy function associated with the proposed hybrid model. Finally, a grid-face structure is designed to explicitly represent the 3D region topology. This representation handles an arbitrary number of regions and facilitates fast surface evolution. Our system was trained and tested on a set of 3D MRI volumes and the results obtained are encouraging. Index Terms — Brain anatomical structures, segmentation, probabilistic boosting tree, discriminative models, generative models I.
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...ations to perform surface evolution, an explicit representation of the regions in necessary. 3-D shape representation is a challenging problem. Some popular methods include parametric representations =-=[35]-=- and finite element representations [6]. The joint priors defined in [5] are used to prevent the surfaces of different objects from intersecting with each other in the level set representation. The le...

Spatio-temporal free-form registration of cardiac MR image sequences

by Dimitrios Perperidis, Raad Mohiaddin, Daniel Rueckert - IN: MICCAI. , 2004
"... In this paper we develop a spatio-temporal registration algorithm for cardiac MR image sequences. The algorithm has the ability to correct any spatial misalignment between the images caused by global differences in the acquisition of the image sequences and by local shape differences. In addition i ..."
Abstract - Cited by 27 (0 self) - Add to MetaCart
In this paper we develop a spatio-temporal registration algorithm for cardiac MR image sequences. The algorithm has the ability to correct any spatial misalignment between the images caused by global differences in the acquisition of the image sequences and by local shape differences. In addition it has the ability to correct temporal misalignment caused by differences in the length of the cardiac cycles and by differences in the dynamic properties of the hearts. The algorithm uses a 4D deformable transformation model which is separated into spatial and temporal components. The registration method was qualitatively evaluated by visual inspection and by measuring the overlap and surface distance of anatomical regions. The results demonstrate that a significant improvement in the alignment of the image sequences is achieved by the use of the deformable transformation model.
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...d. In the recent years cardiac image registration has emerged as an important tool for a large number of applications. It has a fundamental role in the construction of anatomical atlases of the heart =-=[3, 4]-=-. It has also been used for the analysis of the myocardial motion [5] and for the segmentation of cardiac images [6]. Image registration has been also used for the fusion of information from a number ...

I.: A shape-guided deformable model with evolutionary algorithm initialization for 3D soft tissue segmentation

by Tobias Heimann, Sascha Münzing, Hans-peter Meinzer, Ivo Wolf - In: Proc IPMI. Volume 4584 of LNCS , 2007
"... Abstract. We present a novel method for the segmentation of volumetric images, which is especially suitable for highly variable soft tissue structures. Core of the algorithm is a statistical shape model (SSM) of the structure of interest. A global search with an evolutionary algorithm is employed to ..."
Abstract - Cited by 27 (2 self) - Add to MetaCart
Abstract. We present a novel method for the segmentation of volumetric images, which is especially suitable for highly variable soft tissue structures. Core of the algorithm is a statistical shape model (SSM) of the structure of interest. A global search with an evolutionary algorithm is employed to detect suitable initial parameters for the model, which are subsequently optimized by a local search similar to the Active Shape mechanism. After that, a deformable mesh with the same topology as the SSM is used for the final segmentation: While external forces strive to maximize the posterior probability of the mesh given the local appearance around the boundary, internal forces governed by tension and rigidity terms keep the shape similar to the underlying SSM. To prevent outliers and increase robustness, we determine the applied external forces by an algorithm for optimal surface detection with smoothness constraints. The approach is evaluated on 54 CT images of the liver and reaches an average surface distance of 1.6 ± 0.5 mmincomparisonto manual reference segmentations. 1
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...amples. A number of different methods of how to automatically establish the required correspondences in 3D havesbeen proposed in recent years, including registering mesh to mesh [8], volume to volume =-=[9]-=- or mesh to volume [10]. In this work, we employ a population-based approach for finding correspondences which minimizes a cost function based on the description length of the resulting shape model [1...

Shape modeling and analysis with entropybased particle systems

by Joshua Cates - In Proceedings of the 20th International Conference on Information Processing in Medical Imaging , 2007
"... Many important fields of basic research in medicine and biology routinely employ tools for the statistical analysis of collections of similar shapes. Biologists, for example, have long relied on homologous, anatomical landmarks as shape models to characterize the growth and development of species. I ..."
Abstract - Cited by 27 (14 self) - Add to MetaCart
Many important fields of basic research in medicine and biology routinely employ tools for the statistical analysis of collections of similar shapes. Biologists, for example, have long relied on homologous, anatomical landmarks as shape models to characterize the growth and development of species. Increasingly, however, researchers are exploring the use of more detailed models that are derived computationally from three-dimensional images and surface descriptions. While computationally-derived models of shape are promising new tools for biomedical research, they also present some significant engineering challenges, which existing modeling methods have only begun to address. In this dissertation, I propose a new computational framework for statistical shape modeling that significantly advances the state-of-the-art by overcoming many of the limitations of existing methods. The framework uses a particle-system representation of shape, with a fast correspondence-point optimization based on information content. The optimization balances the simplicity of the model (compactness) with the accuracy of the shape representations by using two commensurate entropy
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...ent for the analysis of sets of segmented volumes. Several methods have been proposed that warp a set of images to a reference image, establishing correspondence among images through the deformations =-=[4,5]-=-. These methods are purely image based, however, and do not deal with the problem of selecting surface landmarks for correspondence or establishing geometrically accurate surface samplings. This paper...

Segmentation of the liver using a 3D statistical shape model

by Hans Lamecker, Thomas Lange, Martin Seebaß , 2004
"... This paper presents an automatic approach for segmentation of the liver from computer tomography (CT) images based on a 3D statistical shape model. Segmentation of the liver is an important prerequisite in liver surgery planning. One of the major challenges in building a 3D shape model from a traini ..."
Abstract - Cited by 21 (2 self) - Add to MetaCart
This paper presents an automatic approach for segmentation of the liver from computer tomography (CT) images based on a 3D statistical shape model. Segmentation of the liver is an important prerequisite in liver surgery planning. One of the major challenges in building a 3D shape model from a training set of segmented instances of an object is the determination of the correspondence between different surfaces. We propose to use a geometric approach that is based on minimizing the distortion of the correspondence mapping between two different surfaces. For the adaption of the shape model to the image data a profile model based on the grey value appearance of the liver and its surrounding tissues in contrast enhanced CT data was developed. The robustness of this method results from a previous nonlinear diffusion filtering of the image data. Special focus is turned to the quantitative evaluation of the segmentation process. Several
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... Fleute et al. [15] establish correspondence by elastic registration of a template shape with all other shapes based on minimizing Euclidean distance. Closely related is the approach of Frangi et al. =-=[16]-=-. The PCA is performed on the points of the control grids computed from an elastic registration on binary volumetric data. Brett et al. [17, 18] present two approaches of automatic construction of sha...

R.: 4d shape priors for level set segmentation of the left myocardium in SPECT sequences

by Timo Kohlberger, Daniel Cremers, Mikaël Rousson, Ramamani Ramaraj, Gareth Funka-lea - In: Medical Image Computing and Computer Assisted Intervention. Volume 4190 of LNCS. (2006) 92–100
"... Abstract. We develop a 4D (3D plus time) statistical shape model for implicit level set based shape representations. To this end, we represent hand segmented training sequences of the left ventricle by respective 4-dimensional embedding functions and approximate these by a principal component analys ..."
Abstract - Cited by 16 (1 self) - Add to MetaCart
Abstract. We develop a 4D (3D plus time) statistical shape model for implicit level set based shape representations. To this end, we represent hand segmented training sequences of the left ventricle by respective 4-dimensional embedding functions and approximate these by a principal component analysis. In contrast to recent 4D models on explicit shape representations, the implicit shape model developed in this work does not require the computation of point correspondences which is known to be quite challenging, especially in higher dimensions. Experimental results on the segmentation of SPECT sequences of the left myocardium confirm that the 4D shape model outperforms respective 3D models, because it takes into account a statistical model of the temporal shape evolution. 1
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...hape Priors for a Level Set Segmentation 93 inter-patient shape variabilities. In most cases, Principal Component Analysis (PCA) is the method of choice. In the case of explicit shape representations =-=[4, 9]-=-, PCA is either applied directly to the landmark coordinates or to the components of a deformation field relative to a mean shape, or, for the implicit representation, to the components of the embeddi...

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