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https://cwi-nl.zoom.us/j/85486177209?pwd=4BLXpgKgPgThRm9uJjYeZbs8bMdxL9.1
Meeting ID: 854 8617 7209
Passcode: 813381
Exploiting Symmetries for Personalized Hemodynamics Modeling in Cardiovascular Disease
Large-scale population studies, interactive visualization and diagnosis in the clinic, and medical device design require fast, reliable, and differentiable models for estimation of hemodynamic parameters such as velocity, pressure, and wall shear stress. Commonly used computational fluid dynamics (CFD) approaches are prohibitively time-consuming and complex, leading to interest in the use of deep learning-based methods as a surrogate for CFD. I will discuss how symmetries in imaging and hemodynamics allow us to train deep learning models for cardiovascular segmentation and personalized prediction of hemodynamics efficiently with small real-world data sets. I will touch upon several of our recent works in scale invariant and rotation equivariant artery segmentation for hemodynamics estimation, surrogate modeling for hemodynamic fields estimation, as well as practical considerations in working with small and diverse real-world data sets for learning and validation.
