A minimum description length approach to statistical shape modeling

Davies, Rhodri H.; Twining, Carole J.; Cootes, Timothy F.; Waterton, John C.; Taylor, Chris

doi:10.1109/tmi.2002.1009388

Cited by 462 publications

(284 citation statements)

References 23 publications

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“…Methods based on registration are also very computationally intensive, which may discourage their routine use in the clinical environment. Yet another class of deformable template segmentation methods uses a statistical model of shape and intensity to identify individual anatomical structures (Cootes et al, 1998;Joshi et al, 2002;Davies et al, 2002). The statistical prior model allows these methods to identify structure boundaries in absence of edges of intensity.…”

Section: Previous Workmentioning

confidence: 99%

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

Yushkevich¹,

Piven²,

Hazlett³

et al. 2006

NeuroImage

7,361

4,931

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Active contour segmentation and its robust implementation using level set methods are well established theoretical approaches that have been studied thoroughly in the image analysis literature. Despite the existence of these powerful segmentation methods, the needs of clinical research continue to be fulfilled, to a large extent, using slice-by-slice manual tracing. To bridge the gap between methodological advances and clinical routine, we developed an open source application called ITK-SNAP, which is intended to make level set segmentation easily accessible to a wide range of users, including those with little or no mathematical expertise. This paper describes the methods and software engineering philosophy behind this new tool and provides the results of validation experiments performed in the context of an ongoing child autism neuroimaging study. The validation establishes SNAP intra/interrater reliability and overlap error statistics for the caudate nucleus and finds that SNAP is a highly reliable and efficient alternative to manual tracing.Analogous results for lateral ventricle segmentation are provided.

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Section: Previous Workmentioning

confidence: 99%

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

Yushkevich¹,

Piven²,

Hazlett³

et al. 2006

NeuroImage

7,361

4,931

View full text Add to dashboard Cite

show abstract

“…Kotche↵ and Taylor proposed an algorithm in two dimensions that minimizes the magnitude of the covariance of the correspondences. Davies, et al [30] proposed a information-theoretic cost function of correspondence positions for two-dimensional shapes based on minimum description length (MDL). They later extended the MDL method to three-dimensional shapes [29].…”

Section: Computationally-derived Shape Modelsmentioning

confidence: 99%

“…Particles redistribute on shape surfaces Samples redistribute in shape space Davies, et al [30] propose a similar cost function for two-dimensional shapes based on minimum description length (MDL). The model is optimized by minimizing the cost of transmitting a principal component model of the correspondences.…”

Section: Optimized Correspondencesmentioning

confidence: 99%

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Shape Modeling and Analysis with Entropy-Based Particle Systems

Cates

Fletcher

Styner

et al. 2007

Lecture Notes in Computer Science

139

224

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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 metrics and no free parameters. The idea is to maximize both the geometric accuracy and the statistical simplicity of the shape model, in accordance with the principle of parsimony in model selection. The nonparametric representation allows the method to be applied to a larger class of problems than existing methods, including nonspherical surfaces, open surfaces, and sets of multiple surfaces.The relative simplicity of the surface representation and the low number of free parameters results in a framework that is easy to use and can operate directly on image segmentations. In collaboration with scientists from several important areas of biomedicine, I have demonstrated that the proposed method is indeed an e↵ective tool for scientific research.The specific research contributions of this dissertation are as follows. First, I describe a mathematical framework and a robust numerical algorithm for computing optimized correspondence-point shape models using an entropy-based optimization and particle-system technology. Second, I develop a series of extensions of the framework to more general classes of shape analysis problems, including the analysis of multiple-object complexes, the generalization to correspondence based on generic functions of position, an extension to handle surfaces with open boundaries, and shape modeling with simple regression. Third, I describe the application of statistical hypothesis testing, regression analysis, and multiple-analysis of covariance to the proposed shape models. I also introduce new techniques for visualization and interpretation of these statistics. Finally, in cooperation with biomedical researchers, I present validation of the above research contributions by their successful application to real-world research problems.

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“…More recently, alternative approaches were proposed [25][10] [11] [30]. Evaluating point correspondence between pairs or within a group of shapes is also of interest in the computational anatomy community, which is primarily based on either geodesic distances between corresponding points found by the algorithm and known ground-truth, or via assessing the statistical shape model's generality, specificity, or description length [17][28] [29] [8].…”

mentioning

confidence: 99%

Simulation of Ground-Truth Validation Data Via Physically- and Statistically-Based Warps

Hamarneh

Jassi

Tang

2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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Abstract. The problem of scarcity of ground-truth expert delineations of medical image data is a serious one that impedes the training and validation of medical image analysis techniques. We develop an algorithm for the automatic generation of large databases of annotated images from a single reference dataset. We provide a web-based interface through which the users can upload a reference data set (an image and its corresponding segmentation and landmark points), provide custom setting of parameters, and, following server-side computations, generate and download an arbitrary number of novel ground-truth data, including segmentations, displacement vector fields, intensity nonuniformity maps, and point correspondences. To produce realistic simulated data, we use variational (statistically-based) and vibrational (physically-based) spatial deformations, nonlinear radiometric warps mimicking imaging nonhomogeneity, and additive random noise with different underlying distributions. We outline the algorithmic details, present sample results, and provide the web address to readers for immediate evaluation and usage.

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A minimum description length approach to statistical shape modeling

Cited by 462 publications

References 23 publications

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

Shape Modeling and Analysis with Entropy-Based Particle Systems

Simulation of Ground-Truth Validation Data Via Physically- and Statistically-Based Warps

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