Learning the spatiotemporal variability in longitudinal shape data sets

Bône, Alexandre; Colliot, Olivier; Durrleman, Stanley

doi:10.1007/s11263-020-01343-w

Cited by 16 publications

(13 citation statements)

References 61 publications

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“…We define the Riemannian metric such that each biomarker follow a straight line and the intercept and slopes of the straight lines vary smoothly across neighbourhing regions, voxels or vertices 62 , 63 . We consider changes in the shape of the hippocampus are due to a smooth deformation of the surface called diffeomorphisms 64 , 65 .…”

Section: Methodsmentioning

confidence: 99%

AD Course Map charts Alzheimer’s disease progression

Koval

Bône

Louis

et al. 2021

Sci Rep

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Alzheimer’s disease (AD) is characterized by the progressive alterations seen in brain images which give rise to the onset of various sets of symptoms. The variability in the dynamics of changes in both brain images and cognitive impairments remains poorly understood. This paper introduces AD Course Map a spatiotemporal atlas of Alzheimer’s disease progression. It summarizes the variability in the progression of a series of neuropsychological assessments, the propagation of hypometabolism and cortical thinning across brain regions and the deformation of the shape of the hippocampus. The analysis of these variations highlights strong genetic determinants for the progression, like possible compensatory mechanisms at play during disease progression. AD Course Map also predicts the patient’s cognitive decline with a better accuracy than the 56 methods benchmarked in the open challenge TADPOLE. Finally, AD Course Map is used to simulate cohorts of virtual patients developing Alzheimer’s disease. AD Course Map offers therefore new tools for exploring the progression of AD and personalizing patients care.

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Section: Methodsmentioning

confidence: 99%

AD Course Map charts Alzheimer’s disease progression

Koval

Bône

Louis

et al. 2021

Sci Rep

Self Cite

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“…Further, given covariates of interest, ad hoc analyses may ignore shared information by dividing the dataset into sub-populations of interest and constructing templates for each independently. More principled approaches explicitly account for age and potentially other covariates by building spatiotemporal templates and have been extensively validated on pediatric [34,37,40,55,85,88] and adult [15,28,46,84] neuroimages.…”

Section: Related Workmentioning

confidence: 99%

Generative Adversarial Registration for Improved Conditional Deformable Templates

Dey¹,

Ren²,

Dalca³

et al. 2021

Preprint

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Deformable templates are essential to large-scale medical image registration, segmentation, and population analysis. Current conventional and deep network-based methods for template construction use only regularized registration objectives and often yield templates with blurry and/or anatomically implausible appearance, confounding downstream biomedical interpretation. We reformulate deformable registration and conditional template estimation as an adversarial game wherein we encourage realism in the moved templates with a generative adversarial registration framework conditioned on flexible image covariates. The resulting templates exhibit significant gain in specificity to attributes such as age and disease, better fit underlying group-wise spatiotemporal trends, and achieve improved sharpness and centrality. These improvements enable more accurate population modeling with diverse covariates for standardized downstream analyses and easier anatomical delineation for structures of interest.

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“…In a cohort-based longitudinal study, the variability in inter-subject aging trends can also be high. This was handled in [15] by considering a tubular neighbourhood for the deformation. The spatio-temporal model suggested in [16] also considers similar variations due to diseased data points in the dataset and uses partial least squares regression to compute normal aging deformations; this gives modes of aging and corresponding scores for each subject.…”

Section: Introductionmentioning

confidence: 99%

A Diffeomorphic Aging Model for Adult Human Brain from Cross-Sectional Data

Thottupattu¹,

Sivaswamy²,

Krishnan³

2021

Preprint

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Normative aging trends of the brain can serve as an important reference in the assessment of neurological structural disorders. Such models are typically developed from longitudinal brain image data -follow-up data of the same subject over different time points. In practice, obtaining such longitudinal data is difficult. We propose a method to develop an aging model for a given population, in the absence of longitudinal data, by using images from different subjects at different time points, the so-called cross sectional data. We define an aging model as a diffeomorphic deformation on a structural template derived from the data and propose a method that develops topology preserving aging model close to natural aging.The proposed model is successfully validated on two public cross sectional datasets which provide templates constructed from different sets of subjects at different age points.

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Learning the spatiotemporal variability in longitudinal shape data sets

Cited by 16 publications

References 61 publications

AD Course Map charts Alzheimer’s disease progression

AD Course Map charts Alzheimer’s disease progression

Generative Adversarial Registration for Improved Conditional Deformable Templates

A Diffeomorphic Aging Model for Adult Human Brain from Cross-Sectional Data

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