量化分割不确定性对基于图像的模拟的未知影响
Quantifying the unknown impact of segmentation uncertainty on image-based simulations
摘要：
基于图像的仿真，即使用3D图像来计算物理量，基本上依赖于图像分割来创建计算几何。然而，这个过程引入了图像分割的不确定性，因为有各种不同的分割工具（手动和基于机器学习的），每个工具都将产生唯一和有效的分割。首先，我们证明了这些变化传播到物理模拟中，损害了所得到的物理量。其次，我们提出了一个通用的框架，快速量化分割的不确定性。通过分段不确定性概率图的创建和采样，系统地、客观地创建物理量的不确定性分布。我们表明，物理量的不确定性分布可以遵循正态分布，但是，在更复杂的物理模拟中，所得到的不确定性分布可以是非直观的，令人惊讶的是，非平凡的。我们还建立了简单的边界的不确定性可能会失败的情况下，是敏感的图像分割。虽然我们的工作并没有消除分割的不确定性，但它使以前未被识别的不确定性范围变得可见，目前正在进行基于图像的模拟，从而实现更可信的模拟。
英文摘要：
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.
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