  We propose a novel approach to generate samples from the conditional
distribution of patient-specific cardiovascular models given a clinically
aquired image volume. A convolutional neural network architecture with dropout
layers is first trained for vessel lumen segmentation using a regression
approach, to enable Bayesian estimation of vessel lumen surfaces. This network
is then integrated into a path-planning patient-specific modeling pipeline to
generate families of cardiovascular models. We demonstrate our approach by
quantifying the effect of geometric uncertainty on the hemodynamics for three
patient-specific anatomies, an aorto-iliac bifurcation, an abdominal aortic
aneurysm and a sub-model of the left coronary arteries. A key innovation
introduced in the proposed approach is the ability to learn geometric
uncertainty directly from training data. The results show how geometric
uncertainty produces coefficients of variation comparable to or larger than
other sources of uncertainty for wall shear stress and velocity magnitude, but
has limited impact on pressure. Specifically, this is true for anatomies
characterized by small vessel sizes, and for local vessel lesions seen
infrequently during network training.
