Approches multiéchelles pour la segmentation de très grandes images : application à la quantification de biomarqueurs en histopathologie cancérologique.

Viewing and analyzing automatically sections of cancer tissue are major challenges to progress in the understanding of cancer development and to discover new indicators of response to therapy. The new microscopical scanners provide an essential assistance by supplying high-resolution color images of the whole histological slide. This allows us to overcome the issue of the distribution heterogeneity of the markers which have to be quantified. The size of these images, so-called virtual slides, can reach several gigabytes. The aim of this thesis is to design and to implement a segmentation method in order to isolate and to characterize the various stromal compartments on an ovarian carcinoma virtual slide. The two main difficulties to overcome are the size of images, which prevents us from processing them at once, and the choice of criteria to differentiate the stromal compartments. To address these problems, we developed a generic segmentation framework which combines a smart fragmentation of the image to a characterization of each stromal compartment, regarded as a texture. This characterization is based on a multiscale modeling of textures thanks to a hidden Markov tree model, applied to the wavelet decomposition coefficients. Rather than taking into account all types of stromal compartments at once, we chose to transform the multiclass problem into a set of binary problems. We also analyzed the influence of hyperparameters on segmentation (color representation, wavelet base and order and the number of resolution levels included in the analysis), which allowed us to select the most appropriate classifiers. Several methods of the best classifier decision combinations were then studied. The method was tested on more than twenty virtual slides. To assess the segmentation results, we have taken advantage of a testing protocol based on a stereological approach. Results on various test sets (synthetic images, small images, real images) are presented and discussed. The results obtained on virtual slides are promising, notably if we consider the variability of samples and the difficulty to identify precisely a compartment, even for an expert : about 60% of points are well classified (between 35 % and 80 % according to the slides).Visualiser et analyser automatiquement des coupes fines de tumeurs cancéreuses sont des enjeux majeurs pour progresser dans la compréhension des mécanismes de la cancérisation et mettre en évidence de nouveaux indicateurs de réponse au traitement. Les nouveaux scanners microscopiques apportent une aide essentielle en fournissant des images couleur haute résolution de la totalité des lames histologiques. Ceci permet de s'affranchir de l'hétérogénéité de distribution des marqueurs à quantifier. La taille de ces images, appelées lames virtuelles, peut atteindre plusieurs GigaOctets. L'objectif de cette thèse est de concevoir et d'implémenter une méthode de segmentation permettant de séparer les différents types de compartiments stromaux présents sur une lame virtuelle de carcinome ovarien. Les deux principales difficultés à surmonter sont la taille des images, qui empêche de les traiter en une seule fois, et le choix de critères permettant de différencier les compartiments stromaux. Pour répondre à ces problèmes, nous avons développé une méthode générique de segmentation multiéchelle qui associe un découpage judicieux de l'image à une caractérisation de chaque compartiment stromal, considéré comme une texture. Cette caractérisation repose sur une modélisation multiéchelle des textures par un modèle d'arbre de Markov caché, appliqué sur les coefficients de la décomposition en ondelettes. Plutôt que de considérer tous les types de compartiments stromaux simultanément, nous avons choisi de transformer le problème multiclasse en un ensemble de problèmes binaires. Nous avons également analysé l'influence d'hyperparamètres (représentation couleur, type d'ondelettes et nombre de niveaux de résolutions intégrés à l'analyse) sur la segmentation, ce qui nous a permis de sélectionner les classifieurs les mieux adaptés. Différentes méthodes de combinaison des décisions des meilleurs classifieurs ont ensuite été étudiées. La méthode a été testée sur une vingtaine de lames virtuelles. Afin d'évaluer les résultats de la segmentation, nous avons mis en œuvre un protocole de tests fondé sur une approche stéréologique. Les résultats sur différents jeux de tests (images synthétiques, images de petite taille, images réelles) sont présentés et commentés. Les résultats obtenus sur les lames virtuelles sont prometteurs, compte tenu de la variabilité des échantillons et de la difficulté, pour un expert, à identifier parfois très précisément un compartiment : environ 60% des points sont correctement classés (entre 35% et 80% selon les lames)

Approches multiéchelles pour la segmentation de très grandes images ( application à la quantification de biomarqueurs en histopathologie cancérologique)

Visualiser et analyser automatiquement des coupes fines de tumeurs cancéreuses sont des enjeux majeurs pour améliorer la compréhension des mécanismes de la cancérisation. Les scanners microscopiques haute résolution fournissent des lames virtuelles (de plusieurs Gigaoctets) de la totalité de la lame histologique. Ceci permet de s'affranchir de l'hétérogénéité de distribution des marqueurs à quantifier. Le but de cette thèse est de concevoir une méthode de segmentation des différents types de stroma d'une lame virtuelle de carcinome ovarien. Les obstacles sont la taille des images et le choix de critères permettant de différencier les compartiments stromaux. Pour les contourner, nous proposons une méthode générique de segmentation multiéchelle qui associe un découpage judicieux de l'image à une caractérisation des compartiments considérés comme des textures. Celle-ci repose sur une modélisation multiéchelle des textures par un modèle d'arbre de Markov caché, appliqué aux coefficients de la décomposition en ondelettes. Plutôt que de considérer toutes les classes simultanément, nous avons transformé le problème en un ensemble de problèmes binaires. L'analyse de l'influence d'hyperparamètres sur la segmentation nous a permis de sélectionner les classifieurs les mieux adaptés. Différentes méthodes de combinaison des décisions des meilleurs classifieurs ont ensuite été étudiées. La méthode a été testée sur une vingtaine de lames virtuelles. Les résultats obtenus sont prometteurs, compte tenu de la variabilité des échantillons et de la difficulté à, parfois, identifier très précisément un compartiment. Environ 60% des points sont correctement classés (de 35 à 80% selon la lame).Viewing and analyzing automatically sections of cancer tissue are major challenges to progress in the understanding of cancer development and to discover new indicators of response to therapy. The new microscopical scanners provide an essential assistance by supplying high-resolution color virtual slides of the whole histological slide that can reach several gigabytes. This allows us to overcome the issue of the distribution heterogeneity of the markers which have to be quantified. The aim of this thesis is to design a method in order to segment the various stromal compartments on an ovarian carcinoma virtual slide. The difficulties to overcome are the size of images and the choice of criteria to differentiate the compartments. To tackle these problems, we developed a generic segmentation framework which combines a smart split of the image to a characterization of each compartment, regarded as a texture. This characterization is based on a multiscale modeling of textures thanks to a hidden Markov tree model, applied to the wavelet decomposition coefficients. Rather than considering all classes of compartments at once, the multiclass problem was transformed into a set of binary problems. The influence of hyperparameters on segmentation was also analyzed. This allowed us to select the most appropriate classifiers. Several methods of combination of the best classifier decisions were then studied. The method was tested on more than twenty virtual slides. The results are promising, notably if we consider the variability of samples and the difficulty to identify precisely a compartment: about 60% of points are well classified (between 35 % and 80 % according to the slide).CAEN-BU Sciences et STAPS (141182103) / SudocSudocFranceF

Wavelet-Based Multiscale Texture Segmentation: Application to Stromal Compartment Characterization on Virtual Slides.

International audienceWe aim at segmenting very large images of histopathology virtual slides with an heterogeneous and complex content. To this end, we propose a multiscale framework for texture-based color image segmentation. The core of the method is based on a wavelet-domain hidden Markov tree model and a pairwise classifiers design and selection. The classifier selection is founded on a study of the influence of the hyperparameters of the method used. Over the testing set, majority vote was found to be the best way of combining outputs of the selected classifiers. The method is applied to the segmentation of various types of ovarian carcinoma stroma, on very large virtual slides. This is the first time such a segmentation is tested. The segmentation results are presented and discussed

Automatic Estimation of Tumor Vascularization Using Virtual Slides.

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Problématique d'analyse des grandes images virtuelles de sections histologiques de tumeurs

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Texture-based multiscale segmentation : Application to stromal compartment characterization on avarian carcinoma virtual slides

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Segmentation multi-résolution basée sur la texture ; Application à la segmentation de très grandes images de microscopie cellulaire

International audienceCe papier présente une stratégie de segmentation multi-échelle, mettant en oeuvre un modèle d'arbre de Markov caché appliqué sur les valeurs des coefficients en ondelettes, pour la segmentation de différents types de stroma sur de très grands volumes de données images

Plasmacytoid urothelial carcinoma (UC) are luminal tumors with similar CD8+ Tcell density and PD-L1 protein expression on immune cells as compared to conventional UC

International audienceBackground: Plasmacytoid urothelial carcinoma (UC) is a rare pathological variant of UC with low chemotherapeutic sensitivity and dismal outcomes. The molecular and immune profiles of such tumors remain poorly investigated. Methods: Herein, we investigated the phenotypical features of a cohort of plasmacytoid UC (n=32) by comparison to a control group of conventional high-grade UC with matched clinicopathological characteristics (n=30). Histopathological analysis included the following antibodies: p63, GATA3, CK5/6, CK20 and HER2. In addition, the density of intra-tumor CD8+ lymphocytes, and PD-L1 expression in tumor (TC) and immune cells (IC) were evaluated. Results: Plasmacytoid UC expressed GATA3 (97% vs 86% P=0.18), CK20 (59% vs 36% P=0.08) markers and showed a significantly higher rate of HER2 overexpression (2+ and 3+ score: 25% vs 0%, P<0.01) compared to controls. A significantly lower expression of CK5/6 (22% vs 56%, P<0.05) and p63 (41% vs 80%, P<0.05) was observed in plasmacytoid UC compared to controls. The density of tumor-infiltrating CD8+ cells was similar between plasmacytoid and conventional UC (P=0.9). PD-L1 expression on IC was similar compared to conventional UC (P=0.3). Conclusions: Together, our study demonstrated that plasmacytoid UC belong to the luminal subtype and display a rather inflamed microenvironment similar to conventional UC. These data support the inclusion of plasmacytoid variant of UC in clinical trials evaluating immune checkpoint inhibitors monotherapy or combination immunotherapeutic strategies

KRASG12C inhibition using MRTX1257: a novel radio-sensitizing partner

Abstract Background KRAS activating mutations are considered the most frequent oncogenic drivers and are correlated with radio-resistance in multiple cancers including non-small cell lung cancer (NSCLC) and colorectal cancer. Although KRAS was considered undruggable until recently, several KRAS inhibitors have recently reached clinical development. Among them, MRTX849 (Mirati Therapeutics) showed encouraging clinical outcomes for the treatment of selected patients with KRAS G12C mutated NSCLC and colorectal cancers. In this work, we explore the ability of MRTX1257, a KRASG12C inhibitor analogous to MRTX849, to radio-sensitize KRAS G12C+/+ mutated cell lines and tumors. Methods Both in vitro and in vivo models of radiotherapy (RT) in association with MRTX1257 were used, with different RAS mutational profiles. We assessed in vitro the radio-sensitizing effect of MRTX1257 in CT26 KRASG12C+/+, CT26 WT, LL2 WT and LL2 NRAS KO (LL2 NRAS−/−) cell lines. In vivo, we used syngeneic models of subcutaneous CT26 KRASG12C+/+ tumors in BALB/c mice and T cell deficient athymic nu/nu mice to assess both the radio-sensitizing effect of MRTX1257 and its immunological features. Results MRTX1257 was able to radio-sensitize CT26 KRASG12C+/+ cells in vitro in a time and dose dependent manner. Moreover, RT in association with MRTX1257 in BALB/c mice bearing CT26 KRASG12C+/+ subcutaneous tumors resulted in an observable cure rate of 20%. However, no durable response was observed with similar treatment in athymic nude mice. The analysis of the immune microenvironment of CT26 KRASG12C+/+ tumors following RT and MRTX1257 showed an increase in the proportion of various cell subtypes including conventional CD4 + T cells, dendritic cells type 2 (cDC2) and inflammatory monocytes. Furthermore, the expression of PD-L1 was dramatically down-regulated within both tumor and myeloid cells, thus illustrating the polarization of the tumor microenvironment towards a pro-inflammatory and anti-tumor phenotype following the combined treatment. Conclusion This work is the first to demonstrate in vitro as in vivo the radio-sensitizing effect of MRTX1257, a potent KRASG12C inhibitor compatible with oral administration, in CT26 KRASG12C mutated cell lines and tumors. This is a first step towards the use of new combinatorial strategies using KRAS inhibitors and RT in KRASG12C mutated tumors, which are the most represented in NSCLC with 14% of patients harboring this mutational profile