Manifold modeling for brain population analysis

Gerber, Samuel; Taşdizen, Tolga; Fletcher, P. Thomas; Joshi, Sarang; Whitaker, Ross T.

doi:10.1016/j.media.2010.05.008

Cited by 118 publications

(124 citation statements)

References 37 publications

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“…In the first strategy, as suggested by previous studies (Gerber et al 2010), we used embedding techniques to project the peri-hippocampal VOI i ( ) ω voxel intensities, and the related voxel-wise warp displacements VOI i ( ) F (accounting for ∼17 000 voxels), into low dimensional manifolds. Subsequently, the k atlases nearest to the volume of interest of the validation image VOI v ( ) ω Figure 1.…”

Section: Atlas Selectionmentioning

confidence: 99%

Hippocampal unified multi-atlas network (HUMAN): protocol and scale validation of a novel segmentation tool

et al. 2015

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In this study we present a novel fully automated Hippocampal Unified Multi-Atlas-Networks (HUMAN) algorithm for the segmentation of the hippocampus in structural magnetic resonance imaging. In multi-atlas approaches atlas selection is of crucial importance for the accuracy of the segmentation. Here we present an optimized method based on the definition of a small peri-hippocampal region to target the atlas learning with linear and non-linear embedded manifolds. All atlases were co-registered to a data driven template resulting in a computationally efficient method that requires only one test registration. The optimal atlases identified were used to train dedicated artificial neural networks whose labels were then propagated and fused to obtain the final segmentation. To quantify data heterogeneity and protocol inherent effects, HUMAN was tested on two independent data sets provided by the Alzheimer's Disease Neuroimaging Initiative and the Open Access

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Section: Atlas Selectionmentioning

confidence: 99%

Hippocampal unified multi-atlas network (HUMAN): protocol and scale validation of a novel segmentation tool

et al. 2015

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“…However, these works used linear or logistic regression techniques, which are not necessarily suited to compute statistics on cardiac motion and deformation patterns. For our concrete application, we preferred a formulation based on spectral embedding [11] and kernel regression [3,4] against a Bayesian formulation, in order to minimize the amount of a-priori knowledge in our method.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Infarct Localization from Myocardial Deformation

Duchateau¹,

Sermesant²

2016

Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges

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Abstract. We propose a novel framework to predict the location of a myocardial infarct from local wall deformation data. Non-linear dimensionality reduction is used to estimate the Euclidean space of coordinates encoding deformation patterns. The infarct location of a new subject is inferred by two consecutive interpolations, formulated as multiscale kernel regressions. They consist in (i) finding the low-dimensional coordinates associated to the measured deformation pattern, and (ii) estimating the possible infarct location associated to these coordinates. These concepts were tested on a database of 500 synthetic cases generated from a realistic electromechanical model of the two ventricles. The database consisted of infarcts of random extent, shape, and location overlapping the whole left-anterior-descending coronary territory. We demonstrate that our method is accurate and significantly overcomes the limitations of the clinically-used thresholding of the deformation patterns (average area under the ROC curve of 0.992±0.011 vs. 0.812±0.124, p<0.001).

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“…There has been much interest in identifying and combining additional information in the manifold learning step to improve the resulting embeddings. The manifold structure of brain images has been estimated in [1] based on pairwise non-rigid transformations, whereas in [2] similarities were derived from overlaps of their structural segmentations. In [3], shape and appearance information was combined in a joint embedding for an improved characterization of brain development and in [4] clinical information was incorporated into the embedding construction.…”

Section: Introductionmentioning

confidence: 99%

Learning and Combining Image Similarities for Neonatal Brain Population Studies

Zimmer

Glocker

Aljabar

et al. 2015

Machine Learning in Medical Imaging

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Abstract. The characterization of neurodevelopment is challenging due to the complex structural changes of the brain in early childhood. To analyze the changes in a population across time and to relate them with clinical information, manifold learning techniques can be applied. The neighborhood definition used for constructing manifold representations of the population is crucial for preserving the similarity structure in the embedding and highly application dependent. It has been shown that the combination of several notions of similarity and features can improve the new representation. However, how to combine and weight different similarites and features is non-trivial. In this work, we propose to learn the neighborhood structure and similarity measure used for manifold learning through Neighborhood Approximation Forests (NAFs). The recently proposed NAFs learn a neighborhood structure in a dataset based on a user-defined distance. A characterization of image similarity using NAFs enables us to construct manifold representations based on a previously defined criterion to improve predictions regarding structural and clinical information. In particular, NAFs can be used naturally to combine the affinities learned from multiple distances in a joint manifold towards a more meaningful representation and an improved characterization of the resulting embedding. We demonstrate the utility of NAFs in manifold learning on a population of preterm and in term neonates for classification regarding structural volume and clinical information.

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Manifold modeling for brain population analysis

Cited by 118 publications

References 37 publications

Hippocampal unified multi-atlas network (HUMAN): protocol and scale validation of a novel segmentation tool

Hippocampal unified multi-atlas network (HUMAN): protocol and scale validation of a novel segmentation tool

Prediction of Infarct Localization from Myocardial Deformation

Learning and Combining Image Similarities for Neonatal Brain Population Studies

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