20 research outputs found
De l'imagerie médicale à la modélisation numérique personnalisée du corps humain
National audienceL'imagerie médicale fournit des informations très riches sur l'anatomie et la physiologie d'un patient. L'analyse par ordinateur de ces images permet d'extraire des quantités géométriques, cinématiques ou fonctionnelles. Ces grandeurs peuvent servir à personnaliser des modèles computationnels du corps humain afin qu'ils soient spécifiques à un patient donné. Trois exemples de tels modèles personnalisés sont décrits par la suite
Neural parameters estimation for brain tumor growth modeling
Understanding the dynamics of brain tumor progression is essential for
optimal treatment planning. Cast in a mathematical formulation, it is typically
viewed as evaluation of a system of partial differential equations, wherein the
physiological processes that govern the growth of the tumor are considered. To
personalize the model, i.e. find a relevant set of parameters, with respect to
the tumor dynamics of a particular patient, the model is informed from
empirical data, e.g., medical images obtained from diagnostic modalities, such
as magnetic-resonance imaging. Existing model-observation coupling schemes
require a large number of forward integrations of the biophysical model and
rely on simplifying assumption on the functional form, linking the output of
the model with the image information. In this work, we propose a learning-based
technique for the estimation of tumor growth model parameters from medical
scans. The technique allows for explicit evaluation of the posterior
distribution of the parameters by sequentially training a mixture-density
network, relaxing the constraint on the functional form and reducing the number
of samples necessary to propagate through the forward model for the estimation.
We test the method on synthetic and real scans of rats injected with brain
tumors to calibrate the model and to predict tumor progression
Evaluating glioma growth predictions as a forward ranking problem
The problem of tumor growth prediction is challenging, but promising results
have been achieved with both model-driven and statistical methods. In this
work, we present a framework for the evaluation of growth predictions that
focuses on the spatial infiltration patterns, and specifically evaluating a
prediction of future growth. We propose to frame the problem as a ranking
problem rather than a segmentation problem. Using the average precision as a
metric, we can evaluate the results with segmentations while using the full
spatiotemporal prediction. Furthermore, by separating the model goodness-of-fit
from future predictive performance, we show that in some cases, a better fit of
model parameters does not guarantee a better the predictive power
A generative approach for image-based modeling of tumor growth
22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. ProceedingsExtensive imaging is routinely used in brain tumor patients to monitor the state of the disease and to evaluate therapeutic options. A large number of multi-modal and multi-temporal image volumes is acquired in standard clinical cases, requiring new approaches for comprehensive integration of information from different image sources and different time points. In this work we propose a joint generative model of tumor growth and of image observation that naturally handles multi-modal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.German Academy of Sciences Leopoldina (Fellowship Programme LPDS 2009-10)Academy of Finland (133611)National Institutes of Health (U.S.) (NIBIB NAMIC U54-EB005149)National Institutes of Health (U.S.) (NCRR NAC P41- RR13218)National Institutes of Health (U.S.) (NINDS R01-NS051826)National Institutes of Health (U.S.) (NIH R01-NS052585)National Institutes of Health (U.S.) (NIH R01-EB006758)National Institutes of Health (U.S.) (NIH R01-EB009051)National Institutes of Health (U.S.) (NIH P41-RR014075)National Science Foundation (U.S.) (CAREER Award 0642971
Vers un patient numérique personnalisé pour le diagnostic et la thérapie guidés par l'image [Towards a personalized digital patient for diagnosis and therapy guided by image].
National audienceRecent advances in computer science and medical imaging allow the design of new computational models of the patient which are used to assist physicians. These models, whose parameters are optimized to fit in vivo acquired images, from cells to an entire body, are designed to better quantify the observations (computer aided diagnosis), to simulate the evolution of a pathology (computer aided prognosis), to plan and simulate an intervention to optimize its effects (computer aided therapy), therefore addressing some of the major challenges of medicine of 21(st) century
Image-based modeling of tumor growth in patients with glioma.
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