Analyse de l`angiogénèse tumorale par DCE imagerie

Analyse de l’angiogénèse tumorale par DCE imagerie : Développement de
nouveaux outils pour améliorer la quantification
(see english version on third page
)
Thesis Supervisors: Professor Nikos Paragios ([email protected]), PhD/D.Sc. &
Professor Charles-Andre Cuenod, PhD/D.Sc./MD
École Centrale Paris / Hôpital Européen Georges Pompidou – LRI U970 PARCC HEGP
Abstract : La prise en charge du cancer est un des problèmes majeurs actuellement. Les
techniques d’imagerie à des fins diagnostique et de suivi thérapeutique se sont récemment renforcées
par l’utilisation de techniques fonctionnelles qui permettent d’adjoindre aux critères purement
morphologiques des critères physiologiques qui sont réputés pour être plus sensibles et pour se
modifier plus précocement. Cela ouvre la perspective de diagnostics plus précoces et de suivis
thérapeutiques plus précis. Parmi ces techniques fonctionnelles, les techniques d’imagerie dynamique
avec injection d’agent de contraste (Dynamic Contrast Enhanced Imaging) sous IRM (DCE-MRI),
scanner (DCE-CT) ou échographie (DCE-US) sont les plus fréquemment utilisées. Les résultats
obtenus par ces techniques devraient pouvoir servir de « biomarqueurs » pour suivre les patients et
évaluer les nouveaux traitements antitumoraux. Ces techniques néanmoins souffrent, en conditions
cliniques, de limitations qui les rendent peu reproductibles et peu fiables. Les limitations de ces
techniques basées sur l’analyse de séquences temporelles sont essentiellement due à trois facteurs :
les mouvements physiologiques (respiration, battements cardiaques, mouvements involontaires …), le
faible rapport sur bruit des images individuelles (du fait de temps de poses courts rendu nécessaire
pour l’acquisition dynamique) et la simultanée temporelle des différentes cinétiques qui peuvent se
superposer (perfusion, passage trans-capillaire et diffusion interstielle) et entraîner une
indétermination avec solutions multiples de l’extraction des paramètres de microcirculation.
Objectif
Le but général de ce projet est de construire un « pipeline » complet pour la quantification
automatique de l’angiogénèse tumorale grâce à l’imagerie dynamique de contraste (DCE-Imaging).
L’approche proposée devra remplacer les critères morphologiques (déterminés manuellement) et très
critiques qu’est l’évaluation RECIST, par des biomarqueurs d’imagerie qui exploitent la nature 3D des
tumeurs et leurs dynamiques fonctionnelles.
Afin de répondre à l’objectif clinique décrit ci-dessus, nous devrons introduire de nouvelles méthodes
afin de résoudre les deux problèmes les plus fondamentaux en imagerie biomédicale que sont :
la fusion des images biomédicales, qui consiste à déterminer une méthode de recalage dense
capable de tenir compte des mouvements dus à la respiration, aux battements cardiaques et aux
mouvements des patients. Les méthodes de recalage conventionnelles échouent dans ce domaine du
fait même de la nature dynamique du rehaussement des tissus qui induisent des modifications dans
l’apparence même des images.
La segmentation des images biomédicales qui a comme but de regrouper les voxels de tissus
ayant un comportement fonctionnel similaire, notamment pour améliorer la qualité de l’analyse des
différents comportements et pour fournir une détection automatique des tumeurs ou d’autre tissus
comme la fibrose post-thérapeutique ayant une cohérence de mesures dans le domaine
spatiotemporel.
Programme de Recherche
Ce projet consiste en trois axes scientifiques majeurs : la composante de fusion d’images
déformables, la composante de détection automatique et de segmentation des tissus tumoraux, et la
composante de modélisation fonctionnelle permettant d’extraire les nouveaux biomarqueurs.
- Mois 1-18 : La composante de fusion d’image déformable sera traitée par l’utilisation de modèles
graphiques et d’optimisation discrète. L’idée centrale sera de représenter l’estimation des
déformations en 3D comme un problème de recherche de la solution de coût minimum dans un
graphe. Le challenge scientifique le plus important sera de considérer le problème de fusion
simultanée de tous les volumes acquis à chaque temps et de définir une métrique adéquate.
- Mois 6-24 : Le problème de détection spatiotemporelle des voxels tumoraux et de leur regroupement
sera traité par l’agglomération par clusterisation automatique des signaux temporaux de
rehaussement. L’idée centrale est de procéder à une hiérarchisation des comportements et des
regroupements statistiques.
- Mois 12-30 : La modélisation des données dynamiques obtenues in vivo, ne pourra se concevoir
qu’après les deux étapes précédentes afin de travailler sur des données d’origine non univoque et
corrigés du bruit induit par les mouvements. L’idée principale est de déconvoluer les rehaussements
tissulaire par la fonction d’entrée artérielle en adaptant automatiquement de façon récursive le model
le plus adapté aux donnés à l’aide de critère de qualité. Cette récursivité incluant à terme le mode
d’injection de l’agent de contraste en fonction du type tissulaire voire à l’utilisation d’agents de
contrastes dédiés de poids moléculaires variables. Le but final de la modélisation est l’extraction des
biomarqueurs microcirculatoires.
- Mois 12-30 : Evaluation clinique des biomarqueurs microcirculatoires sur la base de données de
l’étude REMISCAN (étude STIC financée par l’INCA) comportant des acquisitions en DCE-CT, DCEMRI et IRM de diffusion durant le suivi de patients mis sous traitement antiangiogénique pour
métastases de cancer du rein. Les données sont obtenus en multicentriques dans 16 centres
hospitaliers français de façon prospective, incluant les acquisitions avant traitement, à J7 après le
début du traitement et lors du suivi jusqu’à progression tumorale. Ces données sont couplées à
l’acquisition morphologique classique (scanner thoraco-abdomino-pelvien) permettant l’évaluation
selon les critères RECISTS. La robustesse et pertinence des biomarqueurs de microcirculation seront
testées initialement sur cette base. Ultérieurement, les donnés du PHRC Myélome piloté par l’équipe
de l’hôpital Henri Mondor pourront également être testées.
Résultats attendus
Solutions techniques pour répondre aux deux problèmes les plus cruciaux de l’imagerie biomédicale
que sont la fusion d’images déformables et la segmentation. Les solutions à ces problèmes pourront
avoir de très nombreuses applications dans le futur et un important impact clinique.
Développement de biomarqueurs d’imagerie de référence pour l’évaluation et le suivis des tumeurs,
biomarqueurs acquis de façon non invasive et extraits de façon entièrement automatisées grâce à
l’imagerie dynamique avec injection.
Procedural Modeling of architectures towards large-scale image-based
modeling of urban environments
Thesis Supervisor: Professor Nikos Paragios ([email protected]), PhD/D.Sc., École
Centrale Paris
Abstract: The aim of this thesis is to provide a novel, efficient, compact and scalable method for
image-based automatic representation and large-scale modeling of urban scenes. To this end, we
plan to combine efficient representations derived from shape grammars and procedural modeling with
powerful image-based optimization techniques towards automatic 3D parsing of urban environments
from a limited number of 2D images (google street-view like for example). Such an approach offers
compactness (buildings are represented using strings corresponding to the grammar derivation rules),
modularity (different architectures can be expressed through simple modifications of the grammar
rules and its final shapes), scalability (grammars are very powerful representations with limited
complexity and therefore can easily scale) & computational efficiency (grammar-like structured
optimization can be done fast and with certain optimality guarantees leading to very promising results)
while at the same times offers a complete contextual representation of the urban environment (3D
models refer to urban representations where all architectural elements are detected and contextually
represented).
Problem Definition / Motivation / Prior Art
3D large-scale urban modeling has became a major research direction in the recent years due to the
development of the computer games industry, urban planning, navigation & other commercial
services, etc. Existing method exploit a variety of sensors like for example digital elevation maps,
range scanners, aerial imaging and binocular vision cameras towards producing 3D urban models.
Image-based methods which inherit reduced acquisition cost employ conventional geometric
techniques towards recovering surface representation of the scene. The central idea is that if a 3D
point is visible to more than one image and one can determine the exact position of this point in two
different images, then the relative depth of this point can be determined. Recent results on geometric
reconstruction [1] have shown that these methods can now be used for modeling large scale
environments towards producing “triangle-based” representations. The main limitations of these
methods refer to their inability to provide a structured representation of the scene, the sparseness of
the obtained 3D models and their lack of compactness/interpretability (point clouds maps).
Shape grammars [2] is a geometric modeling and interpretation schema that aims at expressing
complex scenes through a syntax like representation that consists of a sequence of rules and a set of
final shapes corresponding to rules that cannot be derived further. This concept is inspired from
conventional grammars, with phonemes being replaced with geometric shapes and syntax rules from
shape composition rulers (as shown in Fig. (1)). Procedural modeling (inspired from shape grammars)
has gained significant attention in the field of computer graphics the past decade [3] towards
automatic generation of urban environments with primary application field the domain of computer
games. The main challenge of such an approach relates to the definition of the grammar which in
most of the cases was done mostly using the end-user expertise (derivation rules, final shapes, etc.).
The use of these concepts to building modeling and reconstruction has gained significant attention
recently, (like for example in [4]). In this context reconstruction can be reformulated as a grammar
derivation problem such that the generated model expresses optimally the observed image according
to a similarity criterion.
Fig. (1) : Grammar derivation sequence for buildings corresponding the Haussmann's architecture.
The transition between 3D modeling and 2D images, the definition of the metric and the optimization
of the grammar derivation sequence can be very challenging when aiming large scale reconstruction
where the camera view point and the geometric/photometric variability of buildings vary significantly.
The grammar optimization is challenging due to the mixed nature of unknown variables since neither
the derivation sequence is known (discrete sequence), nor the corresponding parameters of each rule
(continuous variables). The case of fixed grammar was addressed in [8] through discrete optimization
and in [10] through gradient descent. Reversible-jump Markov chain Monte Carlo allows simulation of
the posterior distribution on spaces of varying dimensions and has been used to optimize grammars
for building reconstruction in [5]. The same principle was applied in [6] using multiple hypothesis
testing while constraining the maximum derivation tree towards computational efficiency while graphbased optimization methods were considered in [7]. In terms of image-driven cost, the use of
geometric proximity is often considered when range data are available [9] while image structure (selfsimilarities) was introduced in [8], mutual information in [10] and automatic [6] or user-based image
classification [7]. Despite the great promise of the above mentioned methods, they inherit two
important limitations:
• The definition of the grammar both in terms of rules, as well as in terms of final shapes is user
specific, an approach that lacks of modularity, and scalability. One can overcome this
limitation using generic grammars which inevitably make their inference from images almost
impossible.
• The image-based grammar inference assumes the camera acquisition parameters either to be
known or considers only 2D grammars, while being computationally and performance-wise
inefficient (convergence to local minima) making the whole processing chain almost not useful
in practice.
Research Goals
In this thesis , following our work in grammar-based large scale urban modeling [6, 7, 8] we would like
to address the above mentioned limitations.
[Work Package 1] In particular, we would like to study the problem of automatic grammar inference
for typologies of architectures from generic purpose grammars. Let us consider without loss of
generality that a number of buildings corresponding to the same class of architecture have been
modeled and reconstructed using a generic grammar. This can be done either with the help of the
user or automatically using one of the methods being developed in [4-10]. The problem of deriving a
representative grammar for an architecture typology can be viewed as a problem of reasoning over
the structure of various graphs representing the different grammar derivation sequences of the
buildings of the training class. In such a context, one can omit the derivation parameters and focus
only to the derivation sequence. Ideally one would expect that in such a sequence, sub-trees will be
repeated across buildings. Such sequences will determine super-rules in the new grammar and will
help to customized generic grammars to architecture specific ones. In order to address such a
concept, we should be able to perform graph-matching with partial correspondences, while taking into
account the nature of interaction between derivation sequences that is usually higher order. To this
end, our research will be based on discrete-optimization methods that do explore dual-decomposition
[11]. The idea is that each sub-tree can be considered as a higher-order clique and one seeks optimal
matching for the entire tree and the individual sub-trees. Due to the compact representation offered by
the grammar, the inclusion of the process of almost all possible trees would be feasible providing
guarantees on the optimality of the process. Once such a matching has been established, the
conventional dimensionality reduction techniques based on graph embeddings can be considered to
determine architecture-specific grammars.
[Work Package 2] The second aspect of our work will be dedicated to pose-invariant image-based
grammar inference from un-calibrated images. In this context, one seeks the optimal 3D configuration
of the grammar and the corresponding image projections such that the distance between the
reconstructed model and its image variant is minimized. Reinforcement Learning comprises a set of
techniques to approximately, but efficiently solve problems that are phrased as Markov Decision
Processes, including Dynamic Programming (DP) and Monte-Carlo as extreme cases. In order to
address the burden of computational complexity, state aggregation methods will be studied that
consist of a finite discretization of the state space into a finite collection of cells where cells do
aggregate the states that fall in this cell. Grammar parsing than can be solved efficiently in a reduced
state space regular techniques. The exploration of cell is critical in such context and if properly
designed could lead to asymptotically converge to the optimal policy, at least under idealized
conditions. The outcome of the automatic grammar inference for typologies of architectures from
generic purpose grammars could provide a natural selection methods regarding optimal exploration of
rules such that cells are visited according to their associated image confidence [being learned at the
training stage].
We are convinced that the proposed approach carries great scientific innovation and could have a
tremendous application impact in a number of domains, such that image-tagging, navigation, urban
modeling, etc.
References
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