Algorithme de détection de ruptures dans un modèle linéaire par morceaux

Cette communication présente un algorithme pour l'identification de paramètres dans un modèle de description de signaux linéaire par morceaux. Dans un premier temps un modèle sans discontinuité est présenté. La méthode d'identification des paramètres se fait au sens des moindres carrés. Le modèle prenant en compte des ruptures possibles dans le signal est ensuite exposé. L'algorithme de complexité quadratique est présenté en détail et enfin un exemple de synthèse est donné

Algorithmes rapides de restauration de signaux avec prise en compte des discontinuités

Cette communication présente des algorithmes pour la restauration des signaux stationnaires par morceaux. Une approche algorithmique basée sur la programmation dynamique conduit à des résultats rapides et optimaux. Deux algorithmes sont présentés. Le premier est d'une mise en oeuvre simple, le second en est une amélioration permettant d'éviter l'énumération de la plupart des états qui ne contribuent pas à l'élaboration de la solution optimale. Ces deux algorithmes fournissent la solution exacte d'un problème d'optimisation non convexe en variable mixte avec des complexités algorithmiques respectives en O(n2) et O(n)

Local classification of microvascular function based on contrast-enhanced ultrasound data: a feasibility study

National audienceDynamic contrast-enhanced ultrasound can detect microvascular flow changes during tumor development and antiangiogenic therapy. However, the standard method for microvascular flow estimation in tumors is global and can lead to bias in flow estimations in heterogeneous tumors. A new method to segment tumors according to their vascularization was investigated. In addition, parameter normalization with respect to a highly vascularized region of reference was proposed to overcome inter-exam variability in parameters. Results demonstrate the potential to locally classify tumoral tissue using parameters that describes the arrival of an ultrasound contrast agent in the tumor

Ascending aorta backward flow parameters estimated from phase-contrast cardiovascular magnetic resonance data: new indices of arterial aging

International audienceOur purpose was to estimate volume and flow rate parameters related to the backward flow in the ascending aorta (AA) using phase-contrast cardiovascular magnetic resonance (PC-CMR) and to evaluate their relationships with age and with well established arterial stiffness indices including wave reflection parameters in an asymptomatic group without overt cardiovascular disease

Nonsupervised Ranking of Different Segmentation Approaches: Application to the Estimation of the Left Ventricular Ejection Fraction From Cardiac Cine MRI Sequences

International audienceA statistical methodology is proposed to rank several estimation methods of a relevant clinical parameter when no gold standard is available. Based on a regression without truth method, the proposed approach was applied to rank eightmethods without using any a priori information regarding the reliability of each method and its degree of automation. It was only based on a prior concerning the statistical distribution of the parameter of interest in the database. The ranking of the methods relies on figures of merit derived from the regression and computed using a bootstrap process. The methodology was applied to the estimation of the left ventricular ejection fraction derived from cardiac magnetic resonance images segmented using eight approaches with different degrees of automation: three segmentations were entirely manually performed and the others were variously automated. The ranking of methods was consistent with the expected performance of the estimation methods: the most accurate estimates of the ejection fraction were obtained using manual segmentations. The robustness of the ranking was demonstrated when at least three methods were compared. These results suggest that the proposed statistical approach might be helpful to assess the performance of estimation methods on clinical data for which no gold standard is available

Improved estimation of the left ventricular ejection fraction using a combination of independent automated segmentation results in cardiovascular magnetic resonance imaging

—This work aimed at combining different segmenta-tion approaches to produce a robust and accurate segmentation result. Three to five segmentation results of the left ventricle were combined using the STAPLE algorithm and the reliability of the resulting segmentation was evaluated in comparison with the result of each individual segmentation method. This comparison was performed using a supervised approach based on a reference method. Then, we used an unsupervised statistical evaluation, the extended Regression Without Truth (eRWT) that ranks different methods according to their accuracy in estimating a specific biomarker in a population. The segmentation accuracy was evaluated by focusing on the left ventricular ejection fraction (LVEF) estimate resulting from the LV contour delineation using a public cardiac cine MRI database. Eight different segmentation methods, including three expert delineations, were studied, and sixteen combinations of the five automated methods were investigated. The supervised and unsupervised evaluations demonstrated that in most cases, STAPLE results provided better estimates of the LVEF than individual automated segmentation methods. In addition, LVEF obtained with STAPLE were within inter-expert variability. Overall, combining different automated segmentation methods improved the reliability of the segmenta-tion result compared to that obtained using an individual metho

Comparison of different segmentation approaches without using gold standard. Application to the estimation of the left ventricle ejection fraction from cardiac cine MRI sequences.

International audienceA statistical method is proposed to compare several estimates of a relevant clinical parameter when no gold standard is available. The method is illustrated by considering the left ventricle ejection fraction derived from cardiac magnetic resonance images and computed using seven approaches with different degrees of automation. The proposed method did not use any a priori regarding with the reliability of each method and its degree of automation. The results showed that the most accurate estimates of the ejection fraction were obtained using manual segmentations, followed by the semiautomatic methods, while the methods with the least user input yielded the least accurate ejection fraction estimates. These results were consistent with the expected performance of the estimation methods, suggesting that the proposed statistical approach might be helpful to assess the performance of estimation methods on clinical data for which no gold standard is available

Deep Learning-based Automated Aortic Area and Distensibility Assessment: The Multi-Ethnic Study of Atherosclerosis (MESA)

This study applies convolutional neural network (CNN)-based automatic segmentation and distensibility measurement of the ascending and descending aorta from 2D phase-contrast cine magnetic resonance imaging (PC-cine MRI) within the large MESA cohort with subsequent assessment on an external cohort of thoracic aortic aneurysm (TAA) patients. 2D PC-cine MRI images of the ascending and descending aorta at the pulmonary artery bifurcation from the MESA study were included. Train, validation, and internal test sets consisted of 1123 studies (24282 images), 374 studies (8067 images), and 375 studies (8069 images), respectively. An external test set of TAAs consisted of 37 studies (3224 images). A U-Net based CNN was constructed, and performance was evaluated utilizing dice coefficient (for segmentation) and concordance correlation coefficients (CCC) of aortic geometric parameters by comparing to manual segmentation and parameter estimation. Dice coefficients for aorta segmentation were 97.6% (CI: 97.5%-97.6%) and 93.6% (84.6%-96.7%) on the internal and external test of TAAs, respectively. CCC for comparison of manual and CNN maximum and minimum ascending aortic areas were 0.97 and 0.95, respectively, on the internal test set and 0.997 and 0.995, respectively, for the external test. CCCs for maximum and minimum descending aortic areas were 0.96 and 0. 98, respectively, on the internal test set and 0.93 and 0.93, respectively, on the external test set. We successfully developed and validated a U-Net based ascending and descending aortic segmentation and distensibility quantification model in a large multi-ethnic database and in an external cohort of TAA patients.Comment: 25 pages, 5 figure