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

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 distri

SUBMITTER: Krygier MC 

PROVIDER: S-EPMC8440761 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature