Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy

Datar, Manasi; Muralidharan, Prasanna; Kumar, Abhishek; Gouttard, Sylvain; Piven, Joseph; Gerig, Guido; Whitaker, Ross T.; Fletcher, P. Thomas

doi:10.1007/978-3-642-33555-6_7

Cited by 15 publications

(17 citation statements)

References 14 publications

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“…Variability is then measured by the displacement of particles between shapes Datar et al (2012, 2009). The major downside is this requires explicit correspondence between shapes, to be able to trace the displacement of a given particle on one shape to its corresponding location on another.…”

Section: Longitudinal Analysis Of Shapementioning

confidence: 99%

Longitudinal modeling of appearance and shape and its potential for clinical use

Gerig

Fishbaugh

Sadeghi

2016

Medical Image Analysis

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Clinical assessment routinely uses terms such as development, growth trajectory, degeneration, disease progression, recovery or prediction. This terminology inherently carries the aspect of dynamic processes, suggesting that single measurements in time and cross-sectional comparison may not sufficiently describe spatiotemporal changes. In view of medical imaging, such tasks encourage subject-specific longitudinal imaging. Whereas follow-up, monitoring and prediction are natural tasks in clinical diagnosis of disease progression and of assessment of therapeutic intervention, translation of methodologies for calculation of temporal profiles from longitudinal data to clinical routine still requires significant research and development efforts. Rapid advances in image acquisition technology with significantly reduced acquisition times and with increase of patient comfort favor repeated imaging over the observation period. In view of serial imaging ranging over multiple years, image acquisition faces the challenging issue of scanner standardization and calibration which is crucial for successful spatiotemporal analysis. Longitudinal 3D data, represented as 4D images, capture time-varying anatomy and function. Such data benefits from dedicated analysis methods and tools that make use of the inherent correlation and causality of repeated acquisitions of the same subject. Availability of such data spawned progress in the development of advanced 4D image analysis methodologies that carry the notion of linear and nonlinear regression, now applied to complex, high-dimensional data such as images, image-derived shapes and structures, or a combination thereof. This paper provides examples of recently developed analysis methodologies for 4D image data, primarily focusing on progress in areas of core expertise of the authors. These include spatiotemporal shape modeling and growth trajectories of white matter fiber tracts demonstrated with examples from ongoing longitudinal clinical neuroimaging studies such as analysis of early brain growth in subjects at risk for mental illness and neurodegeneration in Huntington’s disease (HD). We will discuss broader aspects of current limitations and need for future research in view of data consistency and analysis methodologies.

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Section: Longitudinal Analysis Of Shapementioning

confidence: 99%

Longitudinal modeling of appearance and shape and its potential for clinical use

Gerig

Fishbaugh

Sadeghi

2016

Medical Image Analysis

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“…As in [4], shapes are represented as point distributions in correspondence across subjects and time-points. The joint shape likelihood can be written as…”

Section: The Modelmentioning

confidence: 99%

“…Barry et al [3] build mixed-effects models on a small number of manually selected landmarks to model the development of facial shape. Datar et al [4] build linear mixed-effects models treating shape as a collection of point distribution models in correspondence across subjects. Muralidharan and Fletcher [5] develop a manifold version of a mixed-effects model to analyze longitudinal data taking values on a finite-dimensional Riemannian manifold.…”

Section: Introductionmentioning

confidence: 99%

Bayesian covariate selection in mixed-effects models for longitudinal shape analysis

Muralidharan

Fishbaugh

Kim

et al. 2016

2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)

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The goal of longitudinal shape analysis is to understand how anatomical shape changes over time, in response to biological processes, including growth, aging, or disease. In many imaging studies, it is also critical to understand how these shape changes are affected by other factors, such as sex, disease diagnosis, IQ, etc. Current approaches to longitudinal shape analysis have focused on modeling age-related shape changes, but have not included the ability to handle covariates. In this paper, we present a novel Bayesian mixed-effects shape model that incorporates simultaneous relationships between longitudinal shape data and multiple predictors or covariates to the model. Moreover, we place an Automatic Relevance Determination (ARD) prior on the parameters, that lets us automatically select which covariates are most relevant to the model based on observed data. We evaluate our proposed model and inference procedure on a longitudinal study of Huntington's disease from PREDICT-HD. We first show the utility of the ARD prior for model selection in a univariate modeling of striatal volume, and next we apply the full high-dimensional longitudinal shape model to putamen shapes.

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“…The work presented in Lorenzi et al (2010) [14] measures brain atrophy for each subject using follow up scans, and the method is not based on the LDDMM framework. In Datar et al [6] they used a shape representation based on point to point correspondences and tend to model population trend. In Lorenzi et al (2015) [16] they estimate the anatomical age of a new subject regarding to a normal aging longitudinal model based on stationary vector fields.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Shape Analysis of Cross-Sectional Data for Detection of Early Changes in Neurodegenerative Disease

Cury

Lorenzi

Cash

et al. 2016

Spectral and Shape Analysis in Medical Imaging

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Abstract. The detection of pathological changes in neurodegenerative diseases that occur before clinical onset would be of great value for identifying suitable subjects and assessing drug e cacy in trials aimed at preventing or slowing onset. Using MRI derived volumetric information, researchers have been able to detect significant di↵erences between patients in the presymptomatic phase of neurodegenerative diseases and healthy controls. However, volumetric studies provide only a summary representation of complex morphological changes. Shape analysis has already been successfully applied to model pathological features in neurodegeneration and represents a valuable instrument to model presymptomatic anatomical changes occurring in specific brain regions. In this study we propose a computational framework to model groupwise spatio-temporal shape di↵erences, and to statistically evaluate the e↵ects of time and pathological components on the modeled variability. The proposed approach leverages the geodesic regression framework based on varifolds, and models the spatio-temporal shape variability via dimensionality reduction of the subject-specific "residual" transformations normalised in a common reference frame through parallel transport. The proposed approach is applied to patients with genetic variants of fronto-temporal dementia, and shows that shape di↵erences in the posterior part of the thalamus can be observed several years before the appearance of clinical symptoms.

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Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy

Cited by 15 publications

References 14 publications

Longitudinal modeling of appearance and shape and its potential for clinical use

Longitudinal modeling of appearance and shape and its potential for clinical use

Bayesian covariate selection in mixed-effects models for longitudinal shape analysis

Spatio-Temporal Shape Analysis of Cross-Sectional Data for Detection of Early Changes in Neurodegenerative Disease

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