基于圖像的模拟，即使用三維圖像來計算實體量，從根本上說是依靠圖像分割來建立計算幾何。然而，這個過程引入了圖像分割的不确定性，因為有各種不同的分割工具（包括手動和基于機器學習的），将每個産生一個獨特的和有效的分割。首先，我們證明了這些變化會傳播到實體模拟中，損害了所産生的實體量。其次，我們提出了一個快速量化分割不确定性的一般架構。通過對分段不确定性機率圖的建立和采樣，我們系統地、客觀地建立了實體量的不确定性分布。我們表明，實體量不确定性分布可以遵循正态分布，但是，在較為複雜的實體模拟中，所産生的不确定性分布可能是非直覺的，而且是令人驚訝的非直覺分布。我們還建立了在對圖像分割敏感的情況下，簡單地對不确定性進行限制可能會失敗。雖然我們的工作并沒有消除分割的不确定性，但它使以前沒有認識到的不确定性範圍目前困擾着基于圖像的模拟，使更可信的模拟成為可能。
原文：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.