A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes

Gori, Pietro; Colliot, Olivier; Marrakchi-Kacem, Linda; Worbe, Yulia; Poupon, Cyril; Hartmann, Andreas; Ayache, Nicholas; Durrleman, Stanley

doi:10.1016/j.media.2016.08.011

Cited by 24 publications

(21 citation statements)

References 41 publications

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“…To this aim, digital brain models have been developed in recent years, as a way to synthetize a 3D geometrical model summarizing the anatomical invariants in a group of subjects [566–569]. This model has been extended recently to functional data [570, 571].…”

Section: Spatiotemporal Modeling Of Multimodal Longitudinal Data Analmentioning

confidence: 99%

“…These diagnostic models are based on the Bayesian inference of non-linear mixed-effects models, which complement the usual linear mixed-effects models typically used in biostatistics [569, 572]. This combination of statistical and geometric approaches accounts for the inherent structure in the data such as the specific organization of the brain anatomy as prior knowledge.…”

Section: Spatiotemporal Modeling Of Multimodal Longitudinal Data Analmentioning

confidence: 99%

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Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology

Hampel

Toschi

Babiloni

et al. 2018

JAD

Self Cite

127

113

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The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an “omics”-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND. The goals are to identify common biological denominators or differentiating classifiers across the continuum of ND during detectable stages of pathophysiological progression, characterize systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate development of early and effective individualized treatment of ND, such as Alzheimer’s disease (AD). The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as the Paris based core of the Sorbonne University Clinical Research Group “Alzheimer Precision Medicine” (GRC-APM) were recently launched to facilitate the passageway from conventional clinical diagnostic and drug development towards breakthrough innovation based on the investigation of the comprehensive biological nature of aging individuals. The APMI movement is gaining momentum to systematically apply both systems neurophysiology and systems biology in exploratory translational neuroscience research on ND.

show abstract

Section: Spatiotemporal Modeling Of Multimodal Longitudinal Data Analmentioning

confidence: 99%

Section: Spatiotemporal Modeling Of Multimodal Longitudinal Data Analmentioning

confidence: 99%

Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology

Hampel

Toschi

Babiloni

et al. 2018

JAD

Self Cite

127

113

View full text Add to dashboard Cite

show abstract

“…Subsequently, the models were rigidly aligned to improve the outcome of the procedure. The Bayesian atlas method was used [38] with 108 control points to parametrize the deformations. The resulting template is shown in Figure 3 along with the original models.…”

Section: Model Constructionmentioning

confidence: 99%

Blood Flow Dynamics at the Pulmonary Artery Bifurcation

Capuano

Loke

Balaras

2019

Fluids

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Knowledge of physiologic hemodynamics is a fundamental requirement to establish pathological findings. However, little is known about the normal flow fields in the pulmonary arteries, especially for children. The purpose of this study is to characterize flow patterns in the pulmonary artery bifurcation of healthy pediatric subjects using direct numerical simulations. A realistic geometry is obtained via statistical shape modeling, by averaging five subject-specific digital models extracted from cardiovascular magnetic resonance datasets of healthy volunteers. Boundary conditions are assigned to mimic physiological conditions at rest, corresponding to a peak Reynolds number equal to 3400 and a Womersley number equal to 15. Results show that the normal bifurcation is highly hemodynamically efficient, as measured by an energy dissipation index. The curvature of the pulmonary arteries is sufficiently small to prevent flow separation along the inner walls, and no signs of a turbulent-like state are found. In line with previous imaging studies, a helical structure protruding into the right pulmonary artery is detected, and its formation mechanism is elucidated in the paper. These findings might help to identify abnormal flow features in patients with altered anatomic and physiologic states, particularly those with repaired congenital heart disease.

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“…Furthermore, we propose to model grey matter structures as varifolds [32], [33], the non-oriented extension of the framework of currents [7], [34], or landmarks, if correspondences across subjects are available. The atlas is estimated within a Bayesian framework based on a generative model similar to the one proposed in [35]- [37] and adapted to double diffeomorphisms.…”

Section: B Our Contributionmentioning

confidence: 99%

“…Since the maximization of Eq.3 is not tractable analytically, we use the EM (Expectation Maximization) algorithm where we approximate the conditional distribution of the E step with a Dirac distribution at its mode. See [35], [37] for more information about the E and M step. Assuming that the template T has a non-informative prior distribution, it results:…”

Section: All αmentioning

confidence: 99%

Double Diffeomorphism: Combining Morphometry and Structural Connectivity Analysis

Gori

Colliot

Kacem

et al. 2018

IEEE Trans. Med. Imaging

Self Cite

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A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes

Cited by 24 publications

References 41 publications

Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology

Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology

Blood Flow Dynamics at the Pulmonary Artery Bifurcation

Double Diffeomorphism: Combining Morphometry and Structural Connectivity Analysis

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