  Image-based simulation, the use of 3D images to calculate physical
quantities, fundamentally relies on image segmentation to create the
computational geometry. However, this process introduces image segmentation
uncertainty because there is a variety of different segmentation tools (both
manual and machine-learning-based) that will each produce a unique and valid
segmentation. First, we demonstrate that these variations propagate into the
physics simulations, compromising the resulting physics quantities. Second, we
propose a general framework for rapidly quantifying segmentation uncertainty.
Through the creation and sampling of segmentation uncertainty probability maps,
we systematically and objectively create uncertainty distributions of the
physics quantities. We show that physics quantity uncertainty distributions can
follow a Normal distribution, but, in more complicated physics simulations, the
resulting uncertainty distribution can be both nonintuitive and surprisingly
nontrivial. We also establish that simply bounding the uncertainty can fail in
situations that are sensitive to image segmentation. While our work does not
eliminate segmentation uncertainty, it makes visible the previously
unrecognized range of uncertainty currently plaguing image-based simulation,
enabling more credible simulations.
