Quantifying the unknown impact of segmentation uncertainty on image-based simulations

Michael C. Krygier, Tyler LaBonte, Carianne Martinez, Chance Norris, Krish Sharma, Lincoln N. Collins, Partha P. Mukherjee and Scott A. Roberts ()
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Michael C. Krygier: Sandia National Laboratories
Tyler LaBonte: Sandia National Laboratories
Carianne Martinez: Sandia National Laboratories
Chance Norris: Purdue University
Krish Sharma: Sandia National Laboratories
Lincoln N. Collins: Sandia National Laboratories
Partha P. Mukherjee: Purdue University
Scott A. Roberts: Sandia National Laboratories

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: Abstract Image-based simulation, the use of 3D images to calculate physical quantities, relies on image segmentation for geometry creation. However, this process introduces image segmentation uncertainty because different segmentation tools (both manual and machine-learning-based) 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 surprisingly nontrivial. We establish that bounding segmentation uncertainty can fail in these nontrivial situations. While our work does not eliminate segmentation uncertainty, it improves simulation credibility by making visible the previously unrecognized segmentation uncertainty plaguing image-based simulation.

Date: 2021
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DOI: 10.1038/s41467-021-25493-8

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