Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019).

The overall objective of this project is to design a fully automated and efficient algorithm for finding severalx000D
heart structures in cardiac CINE-MRI imaging data, e.g., the cavities of the left and right ventricles. Computingx000D
such structures translates into comprehensive measures of heart morphology and motion, which are of highx000D
clinical interest in cardiac disease diagnosis. The focus will be on state-of-the-art deep learning approachesx000D
based on convolutional neural networks. Specific sub-objectives include: (1) investigating a novelx000D
semi-supervised learning loss function, which embeds geometric priors accounting for the anatomy of thex000D
heart. The purpose of such priors is to account for the limited size of expert-annotated training data in thex000D
context of cardiac image analysis; (2) designing, implementing and testing a convolutional neural networkx000D
architecture that accounts for the specific context of cardiac structures; and (3) evaluating the algorithm byx000D
comparing the results to ground-truth annotations by cardiologists. This ENGAGE research project will enablex000D
Corstem's algorithms to align with cutting-edge developments in deep learning for medical image analysis, andx000D
to develop stat-of-the-art test examples, with the potential of enhancing its product portfolio andx000D
competitiveness in a fast growing market. Enhancing Corstem's product portfolio with cutting-edge algorithmsx000D
will contribute to Canada's stature as a leading AI and medical imaging technology producer.