Université de Sherbrooke

About: Université de Sherbrooke is a education organization based out in Sherbrooke, Quebec, Canada. It is known for research contribution in the topics: Population & Receptor. The organization has 14922 authors who have published 28783 publications receiving 792511 citations. The organization is also known as: Universite de Sherbrooke & Sherbrooke University.

Impact of Motor, Cognitive, and Perceptual Disorders on Ability to Perform Activities of Daily Living After Stroke

Abstract: Background and Purpose— Using confirmatory factor analysis, this study evaluates the relative impact of motor, cognitive, and perceptual deficits on functional autonomy with 100 elderly (aged 55 to 79 years) victims of stroke. Methods— Two different approaches were used for measuring functional autonomy: the Functional Autonomy Measurement System (Systeme de Mesure de l’Autonomie Fonctionnelle [SMAF]) and the Assessment of Motor and Process Skills (AMPS). Results— The results of the confirmatory factor analysis show that motor, cognitive, and perceptual factors all make a significant contribution to the variation in functional autonomy and confirm the accuracy of the model (93% of the variance is explained when the SMAF is used to measure functional autonomy, and 64% of the variance is explained when the AMPS is used). Conclusions— The factors that make the greatest contribution in explaining the variance in functional autonomy are, in order of importance, the motor factor, the perceptual factor, and the...

Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography

Abstract: Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e segmenting cardiac structures as well as estimating clinical indices, on a dataset especially designed to answer this objective. We therefore introduce the Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMUS) dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6 %. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer's ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images.