Geometric Uncertainty in Patient-Specific Cardiovascular Modeling with Convolutional Dropout Networks

TL;DR

This paper introduces a Bayesian deep learning method using convolutional dropout networks to quantify geometric uncertainty in patient-specific cardiovascular models, impacting hemodynamic predictions.

Contribution

It presents a novel approach to learn geometric uncertainty directly from training data within a cardiovascular modeling pipeline.

Findings

Geometric uncertainty significantly affects wall shear stress and velocity predictions.

Uncertainty impact is limited on pressure estimates.

Method effectively quantifies uncertainty in anatomies with small vessels or lesions.

Abstract

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.