Optimal Transport vs Many-to-many assignment for Graph Matching

National audienceGraph matching for shape comparison or network analysis is a challenging issue in machine learning and computer vision. Gener-ally, this problem is formulated as an assignment task, where we seek the optimal matching between the vertices that minimizes the differencebetween the graphs. We compare a standard approach to perform graph matching, to a slightly-adapted version of regularized optimal transport,initially conceived to obtain the Gromov-Wassersein distance between structured objects (e.g. graphs) with probability masses associated to thenodes. We adapt the latter formulation to undirected and unlabeled graphs of different dimensions, by adding dummy vertices to cast the probleminto an assignment framework. The experiments are performed on randomly generated graphs onto which different spatial transformations areapplied. The results are compared with respect to the matching cost and execution time, showcasing the different limitations and/or advantagesof using these techniques for the comparison of graph networks

Classification of the fibronectin variants with curvelets

International audienceThe role of the extracellular matrix (ECM) in the evolution of certain diseases (e.g. fibrosis, cancer) is generally accepted but yet to be completely understood. A numerical model that captures the physical properties of the ECM, could convey certain connections between the topology of its constituents and their associated biological features. This study addresses the analysis and modeling of fibrillar networks containing Fibronectin (FN) networks, a major ECM molecule, from 2D confocal microscopy images. We leveraged the advantages of the fast discrete curvelet transform (FDCT), in order to obtain a multiscale and multidirectional representation of the FN fibrillar networks. This step was validated by performing a classification among the different variants of FN upregulated in disease states with a multi-class classification algorithm, DAG-SVM. Subsequently, we designed a method to ensure the invariance to rotation of the curvelet features. Our results indicate that the curvelets offer an appropriate discriminative model for the FN networks, that is able to characterize the local fiber geometry

Fibronectin Extra Domains tune cellular responses and confer topographically distinct features to fibril networks

International audienceCellular fibronectin (FN; also known as FN1) variants harboring one or two alternatively spliced so-called extra domains (EDB and EDA) play a central bioregulatory role during development, repair processes and fibrosis. Yet, how the extra domains impact fibrillar assembly and function of the molecule remains unclear. Leveraging a unique biological toolset and image analysis pipeline for direct comparison of the variants, we demonstrate that the presence of one or both extra domains impacts FN assembly, function and physical properties of the matrix. When presented to FN-null fibroblasts, extra domain-containing variants differentially regulate pH homeostasis, survival, and TGF- β by tuning the magnitude of cellular responses, rather than triggering independent molecular switches. Numerical analyses of fiber topologies highlight significant differences in variant-specific structural features and provide a first step for the development of a generative model of FN networks to unravel assembly mechanisms and investigate the physical and functional versatility of extracellular matrix landscapes

The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma

The introduction of the International Association for the Study of Lung Cancer grading system has furthered interest in histopathological grading for risk stratification in lung adenocarcinoma. Complex morphology and high intratumoral heterogeneity present challenges to pathologists, prompting the development of artificial intelligence (AI) methods. Here we developed ANORAK (pyrAmid pooliNg crOss stReam Attention networK), encoding multiresolution inputs with an attention mechanism, to delineate growth patterns from hematoxylin and eosin-stained slides. In 1,372 lung adenocarcinomas across four independent cohorts, AI-based grading was prognostic of disease-free survival, and further assisted pathologists by consistently improving prognostication in stage I tumors. Tumors with discrepant patterns between AI and pathologists had notably higher intratumoral heterogeneity. Furthermore, ANORAK facilitates the morphological and spatial assessment of the acinar pattern, capturing acinus variations with pattern transition. Collectively, our AI method enabled the precision quantification and morphology investigation of growth patterns, reflecting intratumoral histological transitions in lung adenocarcinoma

Characterization of fibronectin networks using graph-based representations of the fibers from 2D confocal images

La fibronectine (FN) cellulaire, composante majeure de la matrice extracellulaire, est organisée en réseaux fibrillaires de maniéré différente suivant les deux extra-domaines EDB et EDA. Notre objectif a été le développement de biomarqueurs quantitatifs pour caractériser l'organisation géométrique des quatre variants de FN à partir d'images de microscopie confocale 2D, puis de comparer les tissus sains et cancéreux. Premièrement, nous avons montré à travers deux pipelines de classification fondés sur les curvelets et sur l'apprentissage profond, que les variants peuvent être distingués avec une performance similaire à celle d'un annotateur humain. Nous avons ensuite construit une représentation des fibres (détectées avec des filtres Gabor) fondée sur des graphes. Les variantes ont été classés en utilisant des attributs spécifiques aux graphes, prouvant que ceux-ci intègrent des informations pertinentes dans les images confocales. De plus, nous avons identifié différentes techniques capables de différencier les graphes, afin de comparer les variants de FN quantitativement et qualitativement. Une analyse des performances sur des exemples simples a montré la capacité des méthodes fondées sur l'appariement de graphes et le transport optimal, de comparer les graphes. Nous avons ensuite proposé différentes méthodologies pour définir le graphe représentatif d'une certaine classe. De plus, l'appariement de graphes nous a permis de calculer des cartes de déformation des paramètres entre tissus sains et cancéreux. Ces cartes ont ensuite été analysées dans un cadre statistique montrant si la variation du paramètre peut être expliquée ou non par la variance au sein d'une même classe.A major constituent of the Extracellular Matrix is a large protein called the Fibronectin (FN). Cellular FN is organized in fibrillar networks and can be assembled differently in the presence of two Extra Domains, EDA and EDB. Our objective was to develop numerical quantitative biomarkers to characterize the geometrical organization of the four FN variants (that differ by the inclusion/exclusion of EDA/EDB) from 2D confocal microscopy images, and to compare sane and cancerous tissues. First, we showed through two classification pipelines, based on curvelet features and deep learning framework, that the FN variants can be distinguished with a similar performance to that of a human annotator. We constructed a graph-based representation of the fibers, which were detected using Gabor filters. Graphspecific attributes were employed to classify the variants, proving that the graph representation embeds relevant information from the confocal images. Furthermore, we identified various techniques capable to differentiate the graphs, allowing us to compare the FN variants quantitatively and qualitatively. Performance analysis using toy graphs showed that the methods, which are based on graph matching and optimal transport, can meaningfully compare graphs. Using the graph-matching framework, we proposed different methodologies for defining the prototype graph, representative of a certain FN class. Additionally, the graph matching served as a tool to compute parameter deformation maps between the variants. These deformation maps were analyzed in a statistical framework showing whether or not the variation of the parameters can be explained by the variance within the same class

Caractérisation des réseaux de fibronectine représentés par des graphes de fibres à partir d'images de microscopie confocale 2D

A major constituent of the Extracellular Matrix is a large protein called the Fibronectin (FN). Cellular FN is organized in fibrillar networks and can be assembled differently in the presence of two Extra Domains, EDA and EDB. Our objective was to develop numerical quantitative biomarkers to characterize the geometrical organization of the four FN variants (that differ by the inclusion/exclusion of EDA/EDB) from 2D confocal microscopy images, and to compare sane and cancerous tissues. First, we showed through two classification pipelines, based on curvelet features and deep learning framework, that the FN variants can be distinguished with a similar performance to that of a human annotator. We constructed a graph-based representation of the fibers, which were detected using Gabor filters. Graphspecific attributes were employed to classify the variants, proving that the graph representation embeds relevant information from the confocal images. Furthermore, we identified various techniques capable to differentiate the graphs, allowing us to compare the FN variants quantitatively and qualitatively. Performance analysis using toy graphs showed that the methods, which are based on graph matching and optimal transport, can meaningfully compare graphs. Using the graph-matching framework, we proposed different methodologies for defining the prototype graph, representative of a certain FN class. Additionally, the graph matching served as a tool to compute parameter deformation maps between the variants. These deformation maps were analyzed in a statistical framework showing whether or not the variation of the parameters can be explained by the variance within the same class.La fibronectine (FN) cellulaire, composante majeure de la matrice extracellulaire, est organisée en réseaux fibrillaires de maniéré différente suivant les deux extra-domaines EDB et EDA. Notre objectif a été le développement de biomarqueurs quantitatifs pour caractériser l'organisation géométrique des quatre variants de FN à partir d'images de microscopie confocale 2D, puis de comparer les tissus sains et cancéreux. Premièrement, nous avons montré à travers deux pipelines de classification fondés sur les curvelets et sur l'apprentissage profond, que les variants peuvent être distingués avec une performance similaire à celle d'un annotateur humain. Nous avons ensuite construit une représentation des fibres (détectées avec des filtres Gabor) fondée sur des graphes. Les variantes ont été classés en utilisant des attributs spécifiques aux graphes, prouvant que ceux-ci intègrent des informations pertinentes dans les images confocales. De plus, nous avons identifié différentes techniques capables de différencier les graphes, afin de comparer les variants de FN quantitativement et qualitativement. Une analyse des performances sur des exemples simples a montré la capacité des méthodes fondées sur l'appariement de graphes et le transport optimal, de comparer les graphes. Nous avons ensuite proposé différentes méthodologies pour définir le graphe représentatif d'une certaine classe. De plus, l'appariement de graphes nous a permis de calculer des cartes de déformation des paramètres entre tissus sains et cancéreux. Ces cartes ont ensuite été analysées dans un cadre statistique montrant si la variation du paramètre peut être expliquée ou non par la variance au sein d'une même classe

Optimal Transport vs Many-to-many assignment for Graph Matching

National audienceGraph matching for shape comparison or network analysis is a challenging issue in machine learning and computer vision. Gener-ally, this problem is formulated as an assignment task, where we seek the optimal matching between the vertices that minimizes the differencebetween the graphs. We compare a standard approach to perform graph matching, to a slightly-adapted version of regularized optimal transport,initially conceived to obtain the Gromov-Wassersein distance between structured objects (e.g. graphs) with probability masses associated to thenodes. We adapt the latter formulation to undirected and unlabeled graphs of different dimensions, by adding dummy vertices to cast the probleminto an assignment framework. The experiments are performed on randomly generated graphs onto which different spatial transformations areapplied. The results are compared with respect to the matching cost and execution time, showcasing the different limitations and/or advantagesof using these techniques for the comparison of graph networks

A spatial statistical framework for the parametric study of fiber networks: application to fibronectin deposition by normal and activated fibroblasts

International audienceDue to the complex architectural diversity of biological networks, there is an increasing need to complement statistical analyses with a qualitative and local description of their spatial properties. One such network is the extracellular matrix (ECM), a biological scaffold for which changes in its spatial organization significantly impact tissue functions in health and disease. Quantifying variations in the fibrillar architecture of major ECM proteins should considerably advance our understanding of the link between tissue structure and function. Inspired from the analysis of functional magnetic resonance imaging (fMRI) images, we propose a novel statistical analysis approach embedded into a machine learning paradigm, to measure and detect local variations of meaningful ECM parameters. We show that parametric maps representing fiber length and pore directionality can be analyzed within the proposed framework to differentiate among various tissue states. The parametric maps are derived from graph-based representations that reflect the network architecture of fibronectin (FN) fibers in a normal, or disease-mimicking in vitro setting. Such tools can potentially lead to a better characterization of dynamic matrix networks within fibrotic tumor microenvironments and contribute to the development of better imaging modalities for monitoring their remodeling and normalization following therapeutic intervention

Cross-stream interactions : segmentation of lung adenocarcinoma growth patterns

Lung adenocarcinoma has histologically distinct growth patterns that have been associated with patient prognosis. Precision segmentation of growth patterns in routine histology samples is challenging due to the complexity of patterns and high intra-class variability. In this paper, we present a novel model with a multi-stream architecture, Cross-Stream Interactions (CroSIn), which fully considers crucial interactions across scales to gather abundant information. The first-order attention introduces contextual information at an early stage to guide low-level feature encoding. The second-order attention then focuses on learning high-level feature relations among scales to extract discriminative features. Experimental results show interactions at both low- and high-level feature learning stages are crucial in performance improvement. The proposed method outperforms state-of-the-art networks, achieving an average Dice of 60.34% at patch level, and an average accuracy of 65.31% at sample level, which is also verified in an independent cohort

The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma.

Funder: UK Medical Research Council (CC2041)The introduction of the International Association for the Study of Lung Cancer grading system has furthered interest in histopathological grading for risk stratification in lung adenocarcinoma. Complex morphology and high intratumoral heterogeneity present challenges to pathologists, prompting the development of artificial intelligence (AI) methods. Here we developed ANORAK (pyrAmid pooliNg crOss stReam Attention networK), encoding multiresolution inputs with an attention mechanism, to delineate growth patterns from hematoxylin and eosin-stained slides. In 1,372 lung adenocarcinomas across four independent cohorts, AI-based grading was prognostic of disease-free survival, and further assisted pathologists by consistently improving prognostication in stage I tumors. Tumors with discrepant patterns between AI and pathologists had notably higher intratumoral heterogeneity. Furthermore, ANORAK facilitates the morphological and spatial assessment of the acinar pattern, capturing acinus variations with pattern transition. Collectively, our AI method enabled the precision quantification and morphology investigation of growth patterns, reflecting intratumoral histological transitions in lung adenocarcinoma