Automated segmentation of cardiac structures in magnetic resonance imaging via deep convolutional neural networks

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