Soutenance de thèse – 3 décembre 2008

Soutenance de thèse – 3 décembre 2008
Détermination automatique des volumes fonctionnels en imagerie d’émission pour les applications en oncologie Mathieu Hatt Sous la direction de Christian Roux et Dimitris Visvikis Thank you mr chairman, ladies and gentlemen, good afternoon . Today I’m going to talk about image segmentation for PET volume determination, INSERM U650 Laboratoire de Traitement de l’Information Médicale (LaTIM) Equipe «Imagerie multi-modalité quantitative pour le diagnostic et la thérapie »

Plan Contexte et motivations Méthodes et données Résultats
Enjeux et imagerie TEP/TDM (PET/CT) Objectif et état de l’art Méthodes et données Approches développées Données de validation et analyse Résultats Optimisation Résultats et applications Discussion et perspectives And here is the outline of my talk. I’ll state the motivation and objective of this work, then l’ll talk about PET image segmentation. I’ll will then present the results we obtained on various datasets, and finally I will conclude and present the studies in progress 2

Imagerie multi-modalité
Contexte et motivations Enjeux Cancer 2002: 11 millions de nouveaux cas et 7 millions de décès Prévisions 2030: 11 millions de décès Oncologie Traitements: Chirurgie Chimiothérapie Radiothérapie Utilisation massive de l’imagerie: Scanner X (TDM) Imagerie par résonance magnétique (IRM) Imagerie d’émission (TEP, TEMP) Souvent combinés TEMP/TDM puis TEP/TDM (2000) Outil de référence pour le diagnostic D’autres applications récentes: Suivi thérapeutique Radiothérapie Imagerie multi-modalité And here is the outline of my talk. I’ll state the motivation and objective of this work, then l’ll talk about PET image segmentation. I’ll will then present the results we obtained on various datasets, and finally I will conclude and present the studies in progress Pour optimiser l’utilisation de l’imagerie dans ce contexte: Correction de divers effets réduisant la qualité des images: résolution spatiale, recalage, mouvements respiratoires, bruit, artefacts… (semi-)automatisation de certaines procédures comme l’extraction de paramètres quantitatifs Exploitation des images encore largement visuelle et manuelle (grande variabilité) L’imagerie quantitative n’est pas exploitée de façon optimale 3

Contexte et motivations
Imagerie TEP Principes physiques de la TEP TEP (Tomographie par Emission de Positons) Principe de base : détection de l’annihilation d’un positon (+) et d’un électron (-) Reconstruction Anneaux de détecteurs Coïncidences ‘lignes de réponse’ And here is the outline of my talk. I’ll state the motivation and objective of this work, then l’ll talk about PET image segmentation. I’ll will then present the results we obtained on various datasets, and finally I will conclude and present the studies in progress 1 à 3 mm 180° +/- 0.25° 4

Imagerie TEP/TDM (PET/CT) Imagerie multi-modalité et fusion Scanner à rayons X (TDM) et scanner TEP avec un seul lit d’examen And here is the outline of my talk. I’ll state the motivation and objective of this work, then l’ll talk about PET image segmentation. I’ll will then present the results we obtained on various datasets, and finally I will conclude and present the studies in progress Philips GEMINI Siemens Biograph GE Discovery LS 5

Imagerie TEP/TDM (PET/CT) Imagerie multi-modalité et fusion And here is the outline of my talk. I’ll state the motivation and objective of this work, then l’ll talk about PET image segmentation. I’ll will then present the results we obtained on various datasets, and finally I will conclude and present the studies in progress 6

Imagerie TEP/TDM (PET/CT) Multi-modalité: bénéfices et inconvénients Utilisation de l’information anatomique pour corriger la fonctionnelle Combinaison de l’information anatomique et fonctionnelle dans un même statif Permet de localiser anatomiquement les fixations détectées sur l’image TEP Recalage parfois incorrect et introduction d’erreurs Absence éventuelle de corrélation entre les structures anatomiques et fonctionnelles Différence de résolution spatiale And here is the outline of my talk. I’ll state the motivation and objective of this work, then l’ll talk about PET image segmentation. I’ll will then present the results we obtained on various datasets, and finally I will conclude and present the studies in progress Coupe axiale TDM Coupe axiale TEP 7

Imagerie TEP/TDM (PET/CT) Problèmes spécifiques à l’imagerie TEP L’imagerie TEP souffre de plusieurs défauts: Résolution spatiale (5 mm) médiocre par rapport à la taille des objets d’intérêt (<1-10 cm) : EVP Bruit important dû à la nature de l’acquisition et à de nombreuses sources d’erreurs Sensibilité aux mouvements respiratoires Scanners et reconstruction: artefacts et bruits spécifiques , échantillonnage spatial image simulée (sans mouvement respiratoire) (cycle respiratoire de 5 sec) image corrigée (transformations élastiques incorporées à la reconstruction) Contraste 8 Contraste 4 2x2 mm 4x4 mm Parcours du positon et non colinéarité 1 à 3 mm 180° +/- 0.25° 10% coincidences diffusées 40% And here is the outline of my talk. I’ll state the motivation and objective of this work, then l’ll talk about PET image segmentation. I’ll will then present the results we obtained on various datasets, and finally I will conclude and present the studies in progress 8

Objectif Volume tumoral 2 Volume tumoral And here is the outline of my talk. I’ll state the motivation and objective of this work, then l’ll talk about PET image segmentation. I’ll will then present the results we obtained on various datasets, and finally I will conclude and present the studies in progress L’objectif est d’obtenir automatiquement ces contours, de façon précise malgré le bruit et le flou robuste par rapport à la grande variabilité des images 9

Etat de l’art L’analyse manuelle souffre d’une grande variabilité intra- et inter-utilisateurs, est longue et fastidieuse (3D) < 2007: la majorité des solutions proposées pour l’analyse semi-automatique utilisent des approches trop simplistes et assez mal validées : plusieurs approches intéressantes ont été publiées, mais elles ne résolvent pas tous les problèmes And here is the outline of my talk. I’ll state the motivation and objective of this work, then l’ll talk about PET image segmentation. I’ll will then present the results we obtained on various datasets, and finally I will conclude and present the studies in progress On peut classer les approches en deux catégories: Utilisant des seuillages Faisant appel à des approches de segmentation d’images plus complexes 10

Etat de l’art Seuillages Un seuil fixe (par exemple 42% du maximum[1]) est inapproprié car très peu robuste aux variations de paramètres (22 mm / 5 min / 8:1) (22 mm / 1 min / 8:1) Sensible au bruit 42% 42% +14% erreur sur le volume -11% Sensible à la taille Sensible au contraste (17 mm / 5 min / 8:1) (22 mm / 1 min / 4:1) And here is the outline of my talk. I’ll state the motivation and objective of this work, then l’ll talk about PET image segmentation. I’ll will then present the results we obtained on various datasets, and finally I will conclude and present the studies in progress 42% 42% +80% +8% [1] N.C. Krak et al, European Journal of Nuclear Medicine and Molecular Imaging, 2005 11

Etat de l’art Seuillages Seuillages adaptatifs Résolution spatiale du scanner et sélection manuelle des tissus [1] Définitions manuelles de régions d’intérêt sur le fond [2] [3] Optimisations pour chaque scanner et reconstruction [1] [2] [3] (en utilisant des acquisitions de sphères homogènes) Nécessitent de nombreuses informations a priori Dépendance à l’utilisateur au système binaire And here is the outline of my talk. I’ll state the motivation and objective of this work, then l’ll talk about PET image segmentation. I’ll will then present the results we obtained on various datasets, and finally I will conclude and present the studies in progress [1] J. A. van Dalen et al, Nuclear Medicine Communications, 2007 [2] U. Nestle et al, Journal of Nuclear Medicine, 2005 [3] J.F. Daisne et al, Radiotherapy Oncology, 2003 12

Etat de l’art Autres approches Fuzzy C-Means W. Zhu et al, IEEE NSS-MIC conference records, 2003 O. Demirkaya, IEEE NSS-MIC conference records, 2003 D. W. G. Montgomery et al, Medical Physics, 2007 Champs de Markov (sans modélisation floue) sur les images d’origine sur les décompositions en ondelettes P. Tylski et al, IEEE NSS-MIC conference records, 2006 Ligne de partage des eaux (« watersheds ») X. Geets et al, European Journal of Nuclear Medicine and Molecular Imaging, 2007 Débruitage & déconvolution puis segmentation par gradient H. Li et al, Medical Physics, 2008 Seuillage adaptatif suivi par un contour actif (modèle déformable) H. Yu et al, IEEE Transactions on Medical Imaging, 2008 Classification basée sur l’analyse de textures et apprentissage d’un arbre de décision And here is the outline of my talk. I’ll state the motivation and objective of this work, then l’ll talk about PET image segmentation. I’ll will then present the results we obtained on various datasets, and finally I will conclude and present the studies in progress Dépendance forte vis-à-vis de pré- ou post-traitements Validées seulement sur des fantômes simplistes, des configurations de contraste ou de bruit irréalistes des tumeurs homogènes ou des données cliniques sans vérité terrain Généralement binaires seulement Performances rarement supérieures à celles de seuillages adaptatifs 13

Méthodes et données Méthodes développées
Hypothèse de travail L’objet d’intérêt à segmenter est déjà identifié et isolé dans une boîte de sélection And here is the outline of my talk. I’ll state the motivation and objective of this work, then l’ll talk about PET image segmentation. I’ll will then present the results we obtained on various datasets, and finally I will conclude and present the studies in progress La boîte de sélection doit : Contenir l’objet entier Contenir suffisamment de fond Elle n’est pas forcément cubique 15

Contexte méthodologique Segmentation statistique d’images 1. Estimer (caché) à partir de (observation disponible) 2. Pas de lien déterministe entre et On utilise une approche probabiliste (inférence bayésienne) Global Modèles de Markov (champs, chaînes, arbres…) Modèle a priori (spatial ou contextuel) Aveugle, contextuel, adaptatif… Local And here is the outline of my talk. I’ll state the motivation and objective of this work, then l’ll talk about PET image segmentation. I’ll will then present the results we obtained on various datasets, and finally I will conclude and present the studies in progress Modèle d’observation (bruit) Gaussien, gaussien généralisé, beta, gamma… déterministe (EM) stochastique (SEM) hybride (ICE) Estimation itérative des paramètres Segmentation Critère MAP, MPM 16

Méthodes et données Méthodes développées Contexte méthodologique
L’aspect probabiliste et statistique permet de prendre en compte l’incertitude de la classification L’aspect flou permet de modéliser l’imprécision inhérente aux données acquises Combiner les deux permet de prendre en compte l’aspect bruité et flou des images d’émission : Mesure de Dirac sur la classe c Modélisation standard “dure” Ground-truth [1] H. Caillol et al, IEEE Transactions on Geoscience Remote Sensing, 1993 [2] F. Salzenstein and W. Pieczynski, Graphical Models and Image Processing, 1997 : Mesure continue de Lesbegue sur Modélisation floue [1,2] Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only Seulement deux classes dures 17

Méthodes et données Méthodes développées … … Chaînes de Markov floues
Définitions Méthodes et données Hypothèse de Markov : Probabilités de transition Probabilités initiales … … Attache aux données Pour passer de l’image (2D ou 3D) à la chaîne (1D), on utilise un parcours fractal d’Hilbert-Peano [1] tout pixel de la chaîne possède comme voisins, deux pixels voisins sur l’image (pas l’inverse) Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only [1] S. Kamata, et al, IEEE Transactions on Image Processing, 1999 18

Chaînes de Markov floues Loi a priori Méthodes et données Dans le contexte d’une chaîne floue, chaque prend ses valeurs dans Hypothèse de chaîne stationnaire. Les densités a priori peuvent être déduites d’une densité jointe définie sur le couple [1] Densités de transitions Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only Avec : Densités initiales [1] F. Salzenstein, C. Collet, S. Lecam, M. Hatt, Pattern Recognition Letters, 2007 19

Méthodes et données Méthodes développées Loi des observations :
Chaînes de Markov floues Loi des observations Méthodes et données Loi des observations : En pratique on opère une discrétisation de l’intervalle [1] On définit alors un certain nombre de niveaux de flou avec des valeurs associées 2 classes dures 0 et 1 de moyennes et variances pour chaque niveau de flou, on détermine les moyennes et variances : Nombre de niveaux de flou et valeurs associées à définir Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only Pour chaque classe dure on peut définir les distributions comme gaussiennes ou d’autres distributions avec le système de Pearson [2] [1] F. Salzenstein, C. Collet, S. Lecam, M. Hatt, Pattern Recognition Letters, 2007 [2] Y. Delignon, et al, IEEE Transactions on Image Processing, 1997 20

Chaînes de Markov floues Segmentation MPM Méthodes et données Segmentation avec le critère MPM [1] adapté au cas flou [2] : la décision bayésienne affectant une étiquette à chaque élément t correspond à : où est une fonction de coût en pratique cela revient à minimiser la fonction : Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only Ce qui nécessite le calcul des densités a posteriori [1] J. Maroquin et al, Journal of the American Statistical Association, 1987 [2] F. Salzenstein and W. Pieczynski, Graphical Models and Image Processing, 1997 21

Chaînes de Markov floues Procédure forward-backward Méthodes et données Procédures forward-backward : calcul récursif direct sur la chaîne [1] … Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only [1] P. Devijver, Pattern Recognition Letters, 1985 22

Chaînes de Markov floues Estimation SEM Méthodes et données Estimation itérative SEM (Stochastic Expectation Maximization) [1] estimation empirique des paramètres par la méthode des moments sur une réalisation a posteriori de X qu’il faut simuler Utilisation des calculs forward-backward pour la simulation: premier élément : transitions : Estimation de tous les paramètres sur cette réalisation : Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only [1] G. Celeux et J. Diebolt, Revue de statistique appliquée, 1986 23

Chaînes de Markov floues Résumé Méthodes et données Vecteur 1D à valeurs réelles : Y Hilbert-Peano 3D Estimation stochastique (SEM) Paramètres estimés : Modèle a priori (probabilités initiales et de transitions) Modèle de bruit (moyennes et variances) Image 3D Hilbert-Peano 3D inverse Carte de segmentation Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only Vecteur 1D à valeurs dans {0,1,F1,F2} : X Segmentation (MPM) [1] M. Hatt et al, Physics in Medicine and Biology, 2007 24

Approche locale adaptative (FLAB) Loi a priori et loi des observations Méthodes et données Chaque prend toujours ses valeurs dans Pas d’hypothèse de Markov : modèle local et non global Probabilités a priori [1] [2] : Indicés par t : prise en compte de la position dans l’image Loi des observations : identique au cas des chaînes Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only [1] H. Caillol et al, IEEE Transactions on Geoscience Remote Sensing, 1993 [2] M. Hatt et al, IEEE Transactions on Medical Imaging, 2008 25

Approche locale adaptative (FLAB) Estimation SEM et segmentation Méthodes et données On utilise le même principe d’estimation que dans le cas des chaînes Nécessité de calculer les probabilités a posteriori de chaque [1] [2] : On peut alors générer une réalisation a posteriori et estimer les paramètres : Cube centré sur le voxel t. Taille à définir ! Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only Information contextuelle prise en compte dans l’estimation On peut également les utiliser pour la segmentation : Calculer pour chaque voxel la probabilité a posteriori Si elle est maximale avec c = 1 ou c = 0, affecter la classe 1 ou 0 Sinon, choisir le niveau de flou qui maximise [1] H. Caillol et al, IEEE Transactions on Geoscience Remote Sensing, 1993, [2] M. Hatt et al, IEEE Transactions on Medical Imaging, 2008 26

Approche locale adaptative (FLAB) Extension à trois classes dures Méthodes et données On modélise les mélanges entre chaque paire de classes dures uniquement 1 3 2 Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only M. Hatt et al, IEEE NSS-MIC conference records, 2007 & 2008 Brevet : FR08 / 56089 27

Méthodes et données Méthodes développées Probabilités a priori :
Approche locale adaptative (FLAB) Extension à trois classes dures Méthodes et données Probabilités a priori : AB transition floue entre classes dures A et B Loi des observations Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only M. Hatt et al, IEEE NSS-MIC conference records, 2007 & 2008 Brevet : FR08 / 56089 28

Méthodes et données Méthodes développées Probabilités a posteriori :
Approche locale adaptative (FLAB) Extension à trois classes dures Méthodes et données Probabilités a posteriori : Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only On peut alors générer une réalisation a posteriori et estimer les paramètres puis segmenter comme dans le cas binaire M. Hatt et al, IEEE NSS-MIC conference records, 2007 & 2008 Brevet : FR08 / 56089 29

Méthodes et données Méthodes développées Estimation stochastique (SEM)
Approche locale adaptative (FLAB) Résumé Méthodes et données Estimation stochastique (SEM) Paramètres estimés : Modèle a priori (probabilités pour chaque voxel) Modèle de bruit (moyennes et variances) Image 3D Carte de segmentation (chaînes) Segmentation Carte de segmentation Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only M. Hatt et al, IEEE NSS-MIC conference records, 2007 & 2008 M. Hatt et al, IEEE Transactions on Medical Imaging, 2008 30

Méthodes et données Méthodes développées Carte de segmentation
Exploitation de la carte de segmentation Carte de segmentation Volume fonctionnel Regroupement Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only Niveaux de flou associés aux voxels affectés par les effets de volume partiel (EVP) : Voxels du fond dont la valeur a été rehaussée Voxels de l’objet dont la valeur a été diminuée M. Hatt et al, IEEE NSS-MIC conference records, 2007 M. Hatt et al, IEEE Transactions on Medical Imaging, 2008 31

Méthodes et données Données de validation
Objectifs et analyse Précédentes publications : lacunes sur la validation Utilisation de données simulées ou de fantômes peu réalistes uniquement Absence de considération de paramètres importants (taille de voxel, bruit, système…) Utilisation de données cliniques sans vérité terrain connue Mesures de performances parfois peu pertinentes Nous voulons valider sur des objets de synthèse et simulés réalistes, sur des acquisitions réelles, et sur des données cliniques pour lesquelles une vérité terrain est disponible Mesure de performance : Vérité terrain Image TEP Segmentation Erreurs Erreur de volume : Erreur de classif. globale : Erreur de classif. : Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only 32

Fantôme Sphères de diamètre 37, 28, 22, 17, 13 et 10 mm Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only 33

A = 4:1 or 5:1, B = 8:1 or 10:1 1 = 2x2 mm, 2 = 4x4 or 5x5 mm
Méthodes et données Données de validation Fantôme : acquisitions Paramètres considérés : contraste sphère/fond : de 4/1 à 10/1 durée d’acquisition : 1, 2 et 5 min taille du voxel : de 2 à 5 mm de côté Scanners (Philips et Philips TF, GE, Siemens) et algorithmes associés (RAMLA, TF MLEM et OSEM) avec protocoles cliniques standards Philips Gemini GE Discovery LS OSEM Siemens Biograph RAMLA Philips Gemini TF TF MLEM A B 1 2 A = 4:1 or 5:1, B = 8:1 or 10: = 2x2 mm, 2 = 4x4 or 5x5 mm Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only 34

Méthodes et données Données de validation Philips GEMINI (RAMLA)
Fantôme : acquisitions Philips GEMINI (RAMLA) Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only 35

Méthodes et données Données de validation Vérité terrain
Objets synthétiques Vérité terrain Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only Contraste 10:5:1 Bruit faible Contraste 10:5:1 Bruit fort Contraste 10:7:4 Bruit faible Contraste 10:7:4 Bruit fort FWHM environ 6 mm Voxels 2x2x2 mm3 36

(Non-Uniform Rational Basis Splines)
Méthodes et données Données de validation Tumeurs simulées : procédure Fantôme NCAT (NURBS) Incorporation Modèle de scanner TEP + Contours manuels Vérité terrain Tumeur NURBS (Non-Uniform Rational Basis Splines) RhinocerosTM TEP Image de patient Simulation GATE et reconstruction Image simulée Calcul d’erreurs Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only TDM Segmentation Carte de segmentation Tumeur simulée Extraction de tumeur 37

Tumeurs simulées : exemples 20 tumeurs (pulmonaires, ORL, hépatiques) diamètre maximum de 12 à 82 mm Hétérogénéité : de aucune à forte Formes : certaines presque sphériques, d’autres de formes complexes Grande et hétérogène Clinique Simulée Clinique Simulée Petite et homogène Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only 38

Tumeurs réelles et histologie 18 tumeurs (pulmonaires) ayant fait l’objet d’une étude macroscopique [1] diamètre maximum de 15 à 90 mm (moyenne 44, écart type 21) Hétérogénéité : de aucune à forte Formes : certaines presque sphériques, d’autres de formes complexes TDM TEP Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only [1] A. Van Baardwijk, et al, International Journal of Radiation Oncology Biolology Physics, 2007 39

Méthodes et données Données de validation 1 2 3 4 Temps Cas 1 8:4:1
Suivi thérapeutique : 8 cas 1 2 3 4 Temps Cas 1 8:4:1 8:4:1 10:7:1 12:1 Cas 3 4:1 4:2,5:1 2,5:1 1,5:1 Cas 5 4:1 6:1 7:2:1 7.5:0.5:1 Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only Cas 6 8:1 6,5:1 4:1 3:1 40

Résultats Optimisation Paramètres à optimiser :
Nombre de niveaux de flou et valeurs associées Type de distribution utilisé pour les observations Taille du cube d’estimation (FLAB uniquement) Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only 2 min 1 min 5 min 42

Résultats Optimisation Les meilleurs résultats sont obtenus avec :
Paramètres Les meilleurs résultats sont obtenus avec : 2 niveaux de flou par transition, avec valeurs et Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only M. Hatt et al, Physics in Medicine and Biology, 2007 43

Paramètres Les meilleurs résultats sont obtenus avec : Distributions gaussiennes (le système de Pearson détecte des lois bêta mais sans amélioration significative des résultats) Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only M. Hatt et al, IEEE Transactions on Medical Imaging, 2008 44

Paramètres Les meilleurs résultats sont obtenus avec : Cube de taille 3x3x3 (pour FLAB) Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only M. Hatt et al, IEEE Transactions on Medical Imaging, 2008 45

Résultats Optimisation
Reproductibilité Sur cinq acquisitions indépendantes de 1 min chacune Ecart type sur les 5 réalisations Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only M. Hatt et al, IEEE Transactions on Medical Imaging, 2008 46

Résultats Optimisation Sur sphères homogènes (4x4x4 mm3) (2x2x2 mm3)
FLAB contre chaînes (FHMC) Sur sphères homogènes (4x4x4 mm3) (2x2x2 mm3) Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only M. Hatt et al, IEEE Transactions on Medical Imaging, 2008 47

Résultats Résultats sur sphères
Robustesse (et précision) Sur l’ensemble des acquisitions de fantôme (tous scanners, algorithmes, paramètres…) Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only M. Hatt et al, IEEE NSS-MIC conference records, 2008 48

Résultats Résultats sur objets synthétiques Vérité terrain
Non binaires Vérité terrain Contraste 10:5:1 Bruit faible Contraste 10:5:1 Bruit fort Contraste 10:7:4 Bruit faible Contraste 10:7:4 Bruit fort T42 20 % 23 % 31 % 27 % Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only T50 83 % 85 % 8 % 9 % 49

Résultats Résultats sur objets synthétiques Vérité terrain
Non binaires Vérité terrain Contraste 10:5:1 Bruit faible Contraste 10:5:1 Bruit fort Contraste 10:7:4 Bruit faible Contraste 10:7:4 Bruit fort Tbckg 15 % 17 % 90 % 38 % Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only 21 % 42 % 21 % 25 % TSBR 50

Résultats Résultats sur objets synthétiques Non binaires
C2 C3 Vérité terrain Contraste 10:5:1 Bruit faible Contraste 10:5:1 Bruit fort Contraste 10:7:4 Bruit faible Contraste 10:7:4 Bruit fort C2 : 11 % C3 : 21 % C2 : 13 % C3 : 19 % C2 : 18 % C3 : 45 % C2 : 30 % C3 : 84 % FCM Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only EC : 4.9 % EC : 7.6 % EC : 6.5 % EC : 9.9 % C2 : 7 % C3 : 7 % C2 : 9 % C3 : 15 % C2 : 9 % C3 : 19 % C2 : 12 % C3 : 27 % FLAB EC : 4.4 % EC : 6.3 % EC : 4.2 % EC : 6.1 % 51

Résultats Résultats sur objets synthétiques Non binaires FCM
Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only Vérité terrain FLAB 52

Résultats Résultats sur tumeurs simulées Exemples Seuillage 42%
Seuillage adaptatif FLAB (2 classes) Segmentation Erreur de classification Vérité terrain TEP simulée > 100% 14% 6% Erreur de classification C2 : 4% C3 : 2% Erreur de volume -62% +37% Segmentation Vérité terrain TEP simulée FLAB (3 classes) Seuillage 42% Seuillage adaptatif C3 C2 Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only M. Hatt et al, IEEE NSS-MIC conference records, 2008 53

Résultats Résultats sur tumeurs simulées
Sur l’ensemble des vingt tumeurs Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only M. Hatt et al, IEEE NSS-MIC conference records, 2008 54

Sur cas de suivi thérapeutique Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only 55

Résultats Résultats sur tumeurs réelles CT Threshold 42% PET
avec histologie : exemple CT Threshold 42% Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only PET Segmentation FLAB Adaptive threshold M. Hatt et al, IEEE NSS-MIC conference records, 2008 59

Résultats Résultats sur tumeurs réelles
avec histologie : sur l’ensemble des 18 tumeurs Now a few words about the fuzzy modeling. On the one hand, the statistical part of the approach models the uncertainty of the classification. On the other hand, the fuzzy part models the imprecision of the acquired data. We combine both parts in order to model PET images characteristics. The statistical measure used in the classical modeling is the following sum of Dirac measures on each class, like illustrated here on a simple ground-truth, and with an associated noisy version of the image and its histogram. In the fuzzy model, we consider two Dirac measures for two hard classes 0 and 1, and a continuous Lesbegue measure on the interval in between to model the distribution of the fuzzy mixture. We called this algorithme FLAB for FUZZY locally adaptive bayesian. Unfortunately this modeling suffers from the limitation of being able to consider two hard classes only, hence homogeneous objects only M. Hatt et al, IEEE NSS-MIC conference records, 2008 60

Discussion et perspectives
Conclusions Méthode de segmentation proposée : Précise, avec performances supérieures aux méthodes de référence Validée sur images synthétiques, acquisitions réelles de fantôme, tumeurs réalistes, données cliniques réelles avec histologie Robuste et reproductible, utilisable sans optimisation sur différents systèmes Capacité de produire des volumes segmentés non binaires Intervention de l’utilisateur réduite au minimum mais possible Temps de calculs négligeables Radiothérapie guidée par l’image (volumes biologiques) : Projet ANR SIFR (2 ans, ) Validation sur données cliniques et histologie (volume entier et pas seulement diamètre) Implémentation de FLAB dans une station de planification et étude de son impact sur la pratique et la dosimétrie To conclude, we have developed a fully automatic approach for volume determination in PET. It is capable of handling non homogeneous uptake and non spherical shapes, and also providing non binary segmented volumes, which can be useful for example for dose painting applications. Its robustness was evaluated using multiple phantom acquisitions demonstrating the algorithm does not require specific system-dependent optimization. Its accuracy was assessed on both simulated and real tumour. Diagnostic et suivi thérapeutique : nécessite quantification en plus des volumes 62

Quantification Obtenir le volume exact ne suffit pas ! Nécessité de combiner avec la correction quantitative des effets de volume partiel (méthode de Rousset ou MMA) To conclude, we have developed a fully automatic approach for volume determination in PET. It is capable of handling non homogeneous uptake and non spherical shapes, and also providing non binary segmented volumes, which can be useful for example for dose painting applications. Its robustness was evaluated using multiple phantom acquisitions demonstrating the algorithm does not require specific system-dependent optimization. Its accuracy was assessed on both simulated and real tumour. 63

Limites : Pas de détection automatique du nombre de classes Seulement 3 classes dures : problème si l’activité dans la tumeur ET le fond est très hétérogène Cas difficilement automatisables : Tumeurs collées à des fixations non pathologiques et dont l’activité est du même niveau d’intensité (ajout d’informations a priori nécessaire) Problèmes liés à l’inflammation (spécificité du FDG) Multiples lésions très proches les unes des autres et de fixations différentes To conclude, we have developed a fully automatic approach for volume determination in PET. It is capable of handling non homogeneous uptake and non spherical shapes, and also providing non binary segmented volumes, which can be useful for example for dose painting applications. Its robustness was evaluated using multiple phantom acquisitions demonstrating the algorithm does not require specific system-dependent optimization. Its accuracy was assessed on both simulated and real tumour. 64

Meilleure automatisation : Détection automatique de la tumeur dans l’image Détection automatique du nombre de classes Développements possibles : Prise en compte de l’information anatomique (par modèle à plusieurs observations) Ajout de l’information d’autres traceurs (FMISO, FLT…) pour volume biologique Modèles de Markov couples/triplet (flous) ? To conclude, we have developed a fully automatic approach for volume determination in PET. It is capable of handling non homogeneous uptake and non spherical shapes, and also providing non binary segmented volumes, which can be useful for example for dose painting applications. Its robustness was evaluated using multiple phantom acquisitions demonstrating the algorithm does not require specific system-dependent optimization. Its accuracy was assessed on both simulated and real tumour. 65

Merci à tous pour votre attention
Remerciements plus particuliers à Christian Roux Dimitris Visvikis Catherine Cheze Le Rest Wojiech Pieczynski et Roland Hustinx Olivier Pradier Toute l’équipe du LaTIM Ma famille Mes amis Thank you for your attention. I will now gladly answer any question you may have. Travaux financés par la région Bretagne, et l’Institut Telecom 66

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