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159
A Generative Model for Image Segmentation Based on Label Fusion
- IEEE TRANSACTIONS IN MEDICAL IMAGING
, 2010
"... We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels ..."
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Cited by 62 (5 self)
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We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute the final segmentation of the test subject. Such label fusion methods have been shown to yield accurate segmentation, since the use of multiple registrations captures greater inter-subject anatomical variability and improves robustness against occasional registration failures. To the best of our knowledge, this manuscript presents the first comprehensive probabilistic framework that rigorously motivates label fusion as a segmentation approach. The proposed framework allows us to compare different label fusion algorithms theoretically and practically. In particular, recent label fusion or multiatlas segmentation
Deformable medical image registration: A survey
- IEEE TRANSACTIONS ON MEDICAL IMAGING
, 2013
"... Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudin ..."
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Cited by 35 (1 self)
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Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudinal studies, where temporal structural or anatomical changes are investigated; and iii) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.
Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. Neuroimage
, 2010
"... Keywords: Brain cortical thickness Brain surface area Heritability Choosing the appropriate neuroimaging phenotype is critical to successfully identify genes that influence brain structure or function. While neuroimaging methods provide numerous potential phenotypes, their role for imaging genetics ..."
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Cited by 26 (4 self)
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Keywords: Brain cortical thickness Brain surface area Heritability Choosing the appropriate neuroimaging phenotype is critical to successfully identify genes that influence brain structure or function. While neuroimaging methods provide numerous potential phenotypes, their role for imaging genetics studies is unclear. Here we examine the relationship between brain volume, grey matter volume, cortical thickness and surface area, from a genetic standpoint. Four hundred and eighty-six individuals from randomly ascertained extended pedigrees with high-quality T1-weighted neuroanatomic MRI images participated in the study. Surface-based and voxel-based representations of brain structure were derived, using automated methods, and these measurements were analysed using a variance-components method to identify the heritability of these traits and their genetic correlations. All neuroanatomic traits were significantly influenced by genetic factors. Cortical thickness and surface area measurements were found to be genetically and phenotypically independent. While both thickness and area influenced volume measurements of cortical grey matter, volume was more closely related to surface area than cortical thickness. This trend was observed for both the volume-based and surface-based techniques. The results suggest that surface area and cortical thickness measurements should be considered separately and preferred over gray matter volumes for imaging genetic studies.
Multi-contrast large deformation diffeomorphic metric mapping for diffusion tensor imaging
- NEUROIMAGE 47 (2009) 618–627
, 2009
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P.: Regression-Based Label Fusion for Multi-Atlas Segmentation
, 2012
"... Automatic segmentation using multi-atlas label fusion has been widely applied in medical image analysis. To simplify the label fusion problem, most methods implicitly make a strong assumption that the segmentation errors produced by different atlases are uncorrelated. We show that violating this ass ..."
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Cited by 12 (4 self)
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Automatic segmentation using multi-atlas label fusion has been widely applied in medical image analysis. To simplify the label fusion problem, most methods implicitly make a strong assumption that the segmentation errors produced by different atlases are uncorrelated. We show that violating this assumption significantly reduces the efficiency of multi-atlas segmentation. To address this problem, we propose a regression-based approach for label fusion. Our experiments on segmenting the hippocampus in magnetic resonance images (MRI) show significant improvement over previous label fusion techniques. 1.
Image Similarity and Tissue Overlaps as Surrogates for Image Registration Accuracy: Widely Used but Unreliable
"... Abstract—The accuracy of nonrigid image registrations is commonly approximated using surrogate measures such as tissue label overlap scores, image similarity, image difference, or transformation inverse consistency error. This paper provides experimental evidence that these measures, even when used ..."
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Cited by 12 (0 self)
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Abstract—The accuracy of nonrigid image registrations is commonly approximated using surrogate measures such as tissue label overlap scores, image similarity, image difference, or transformation inverse consistency error. This paper provides experimental evidence that these measures, even when used in combination, cannot distinguish accurate from inaccurate registrations. To this end, we introduce a “registration ” algorithm that generates highly inaccurate image transformations, yet performs extremely well in terms of the surrogate measures. Of the tested criteria, only overlap scores of localized anatomical regions reliably distinguish reasonable from inaccurate registrations, whereas image similarity and tissue overlap do not. We conclude that tissue overlap and image similarity, whether used alone or together, do not provide valid evidence for accurate registrations and should thus not be reported or accepted as such. Index Terms—Nonrigid image registration, registration accuracy, unreliable surrogates, validation. I.
Nonrigid registration of medical images: Theory, methods, and applications
- IEEE Signal Processing Magazine
, 2010
"... Medical mage registration [1] plays an increasingly important role in many clinical applications including the detection and diagnosis of diseases, the planning of therapy, the guidance of interven-tions and the follow-up and monitoring of patients. The primary goal of image registration is to find ..."
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Cited by 8 (0 self)
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Medical mage registration [1] plays an increasingly important role in many clinical applications including the detection and diagnosis of diseases, the planning of therapy, the guidance of interven-tions and the follow-up and monitoring of patients. The primary goal of image registration is to find corresponding anatomical or functional locations in two or more images. This has many applications: Registration can be applied to images from the same subject acquired by different imaging modalities (multi-modal image registration) or at different time points (serial image registration). Both cases are examples of intra-subject registration since the images are acquired from the same subject. Another application area for image registration is inter-subject registration where the aim is to align images acquired from different subjects, e.g. to study the anatomical variability within or across populations. While rigid registration has become a widely used tool in clinical practice, non-rigid registration has not yet achieved the same level of acceptance. Much recent progress has been made, however, in developing improved non-rigid registration techniques. In this article we will illustrate some of the advances which have been made over the last decades. We will discuss some of the theoretical aspects of non-rigid registration and describe methods for their implementation. Finally, we will illustrate how common problems in medical imaging, such as motion correction and image segmentation, can
andN.Ayache, “Registration of 4D cardiac CT sequences under trajectory constraints with multichannel diffeomorphic demons
- IEEE Transactions on Medical Imaging
, 2010
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Asymmetric Image-Template Registration
, 2009
"... A natural requirement in pairwise image registration is that the resulting deformation is independent of the order of the images. This constraint is typically achieved via a symmetric cost function and has been shown to reduce the effects of local optima. Consequently, symmetric registration has bee ..."
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Cited by 7 (2 self)
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A natural requirement in pairwise image registration is that the resulting deformation is independent of the order of the images. This constraint is typically achieved via a symmetric cost function and has been shown to reduce the effects of local optima. Consequently, symmetric registration has been successfully applied to pairwise image registration as well as the spatial alignment of individual images with a template. However, recent work has shown that the relationship between an image and a template is fundamentally asymmetric. In this paper, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in imagetemplate registration improves alignment in the image coordinates.
The New State of the
- Mass psychology in the White House. Society (Transaction: Social Scienceond Modern Society
, 1977
"... wave functions and energies for nonzero angular momentum ..."
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Cited by 7 (0 self)
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wave functions and energies for nonzero angular momentum
