Reference number : GC-2017-Q4-00150, amendment number : , agreement type : G, recipient legal name : Ben Ayed, Ismail (École de technologie supérieure), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : The overall objective of this project is to design a fully automated and efficient algorithm for finding several_x000D_
heart structures in cardiac CINE-MRI imaging data, e.g., the cavities of the left and right ventricles. Computing_x000D_
such structures translates into comprehensive measures of heart morphology and motion, which are of high_x000D_
clinical interest in cardiac disease diagnosis. The focus will be on state-of-the-art deep learning approaches_x000D_
based on convolutional neural networks. Specific sub-objectives include: (1) investigating a novel_x000D_
semi-supervised learning loss function, which embeds geometric priors accounting for the anatomy of the_x000D_
heart. The purpose of such priors is to account for the limited size of expert-annotated training data in the_x000D_
context of cardiac image analysis; (2) designing, implementing and testing a convolutional neural network_x000D_
architecture that accounts for the specific context of cardiac structures; and (3) evaluating the algorithm by_x000D_
comparing the results to ground-truth annotations by cardiologists. This ENGAGE research project will enable_x000D_
Corstem's algorithms to align with cutting-edge developments in deep learning for medical image analysis, and_x000D_
to develop stat-of-the-art test examples, with the potential of enhancing its product portfolio and_x000D_
competitiveness in a fast growing market. Enhancing Corstem's product portfolio with cutting-edge algorithms_x000D_
will contribute to Canada's stature as a leading AI and medical imaging technology producer., program purpose french : The overall objective of this project is to design a fully automated and efficient algorithm for finding several_x000D_
heart structures in cardiac CINE-MRI imaging data, e.g., the cavities of the left and right ventricles. Computing_x000D_
such structures translates into comprehensive measures of heart morphology and motion, which are of high_x000D_
clinical interest in cardiac disease diagnosis. The focus will be on state-of-the-art deep learning approaches_x000D_
based on convolutional neural networks. Specific sub-objectives include: (1) investigating a novel_x000D_
semi-supervised learning loss function, which embeds geometric priors accounting for the anatomy of the_x000D_
heart. The purpose of such priors is to account for the limited size of expert-annotated training data in the_x000D_
context of cardiac image analysis; (2) designing, implementing and testing a convolutional neural network_x000D_
architecture that accounts for the specific context of cardiac structures; and (3) evaluating the algorithm by_x000D_
comparing the results to ground-truth annotations by cardiologists. This ENGAGE research project will enable_x000D_
Corstem's algorithms to align with cutting-edge developments in deep learning for medical image analysis, and_x000D_
to develop stat-of-the-art test examples, with the potential of enhancing its product portfolio and_x000D_
competitiveness in a fast growing market. Enhancing Corstem's product portfolio with cutting-edge algorithms_x000D_
will contribute to Canada's stature as a leading AI and medical imaging technology producer., agreement title english : Automated segmentation of cardiac structures in magnetic resonance imaging via deep convolutional neural networks, agreement title french : Automated segmentation of cardiac structures in magnetic resonance imaging via deep convolutional neural networks, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-02-07, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada