A Dynamic Brain Atlas

Hill, Derek L. G.; Hajnal, Joseph V.; Rueckert, Daniel; Smith, Stephen M.; Hartkens, Thomas; McLeish, Kate

doi:10.1007/3-540-45786-0_66

Cited by 16 publications

(9 citation statements)

References 9 publications

(5 reference statements)

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“…Several authors [6, 7, 8] have proposed kernel-based regression of brain images. The main distinction of the work described in this paper is that the underlying parametrization is learned from the image data.…”

Section: Related Workmentioning

confidence: 99%

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Manifold modeling for brain population analysis

Gerber

Taşdizen

Fletcher

et al. 2010

Medical Image Analysis

118

122

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This paper describes a method for building efficient representations of large sets of brain images. Our hypothesis is that the space spanned by a set of brain images can be captured, to a close approximation, by a low-dimensional, nonlinear manifold. This paper presents a method to learn such a low-dimensional manifold from a given data set. The manifold model is generative-brain images can be constructed from a relatively small set of parameters, and new brain images can be projected onto the manifold. This allows to quantify the geometric accuracy of the manifold approximation in terms of projection distance. The manifold coordinates induce a Euclidean coordinate system on the population data that can be used to perform statistical analysis of the population. We evaluate the proposed method on the OASIS and ADNI brain databases of head MR images in two ways. First, the geometric fit of the method is qualitatively and quantitatively evaluated. Second, the ability of the brain manifold model to explain clinical measures is analyzed by linear regression in the manifold coordinate space. The regression models show that the manifold model is a statistically significant descriptor of clinical parameters.

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Section: Related Workmentioning

confidence: 99%

“…Each template represents a part of the population. In a different direction, researchers proposed kernel-based regression of brain images with respect to an underlying parameter [6, 7, 8]. This yields a continuous curve in the space of brain images that estimates the conditional expectation of a brain image given the parameter value.…”

Section: Introductionmentioning

confidence: 99%

Manifold modeling for brain population analysis

Gerber

Taşdizen

Fletcher

et al. 2010

Medical Image Analysis

118

122

View full text Add to dashboard Cite

show abstract

“…[9] is a probabilistic approach combining a standard EM-based segmentation [10] with a dynamic brain atlas construction [11].…”

Section: Expectation-maximisation-based Segmentation Using a Dynamic mentioning

confidence: 99%

Comparison and Evaluation of Segmentation Techniques for Subcortical Structures in Brain MRI

Babalola

Patenaude

Aljabar

et al. 2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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Abstract. The automation of segmentation of medical images is an active research area. However, there has been criticism of the standard of evaluation of methods. We have comprehensively evaluated four novel methods of automatically segmenting subcortical structures using volumetric, spatial overlap and distance-based measures. Two of the methods are atlas-based -classifier fusion and labelling (CFL) and expectationmaximisation segmentation using a dynamic brain atlas (EMS), and two model-based -profile active appearance models (PAM) and Bayesian appearance models (BAM). Each method was applied to the segmentation of 18 subcortical structures in 270 subjects from a diverse pool varying in age, disease, sex and image acquisition parameters. Our results showed that all four methods perform on par with recently published methods. CFL performed significantly better than the other three methods according to all three classes of metrics.

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“…Grid computing technology is a rapidly evolving field of interest and has been experiments have been undertaken in a wide range of areas such as particle physics, multi-scale modeling, computational steering, finance, defense research, drug discovery, decision-making, and collaborative design. The Grid applications for medical imaging analysis have also been reported [4,5]. It has been shown that the Grid can tackle complex problems in reasonable time scales and with convincing performance and this has further push the development of Grid technologies.…”

Section: Introductionmentioning

confidence: 97%

Alignment of dynamic contrast-enhanced MR volumes of the breast for a multicenter trial: an exemplar grid application

Zheng

Tanner²,

Hill

et al. 2004

SPIE Proceedings

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Image registration is a very important procedure in medical imaging analysis. However, the intensive computations involved in image registration have to some extent made it impractical for interactive use as well as limiting its general availability. This paper presents our current Grid project to facilitate image registration tasks. We have set up an image registration Grid by combining the attractive features of both Globus and Condor distributed computing environments. In order to make it more convenient to use, we have also developed a web interface for potential clients to specify and submit their image registration jobs to the Grid. The initial experiments in 3D breast MR images have shown encouraging results and demonstrated the suitability of Grid technology to this type of computationally intensive applications. The image registration Grid makes it much more straightforward for different institutes to use the identical registration program and protocols to register images consistently, quickly and efficiently. This can greatly improve data sharing and comparative studies in multi-centre trials. The Grid presented here could be an important step for clinical applications of image registration. Future work will focus on refining the Grid with the aim of upgrading it to a Grid Service and testing the system more extensively with medical imaging dataset.Image registration is an important procedure in medical imaging analysis. The purpose of image registration is to align one image (source image) to another (target or reference image) so that the possible misalignments between them can be minimized or eliminated, thus establishing spatial correspondence. Proper registration enables us to have a better understanding of the features of interest and integration of useful information. Applications are wide: ranging from image-guided surgery to atlas construction, segmentation propagation, monitoring changes over time and dynamic sequence analysis. The general procedure of fully automated image registration typically requires optimization of a function of the similarity between the target and source images. A large variety of image registration methods have been proposed for medical applications. In practice, image registration can be further classified as rigid and non-rigid according to the underlying transformation model. The former registers images by assuming that images are misaligned by translations and rotations only while the latter allows many more degrees of freedom. Compared to rigid registration, non-rigid registration can theoretically model more complicated deformations but requires more computation [1,2]. Rigid registration is normally used to provide a starting estimate before non-rigid registration in order to reduce computational time. Even so non-rigid registration still requires massive computational time, e.g. an average computation time up to 5 hours has been reported to register 3-D MR images of a single breast with a typical region of

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A Dynamic Brain Atlas

Cited by 16 publications

References 9 publications

Manifold modeling for brain population analysis

Manifold modeling for brain population analysis

Comparison and Evaluation of Segmentation Techniques for Subcortical Structures in Brain MRI

Alignment of dynamic contrast-enhanced MR volumes of the breast for a multicenter trial: an exemplar grid application

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