Quantifying the Unknown: Impact of Segmentation Uncertainty on Image-Based Simulations - Data

Published: 11 May 2021| Version 3 | DOI: 10.17632/g3hr4rkb48.3
Michael Krygier,
Tyler LaBonte,
Carianne Martinez,
Chance Norris,
Krish Sharma,
Lincoln Collins,
Partha Mukherjee


This repository contains data and scripts associated with the manuscript ``Quantifying the Unknown: Impact of Segmentation Uncertainty on Image-Based Simulations.'' Data is organized into each of the three exemplars shown in the paper. Probability map datasets are provided as gzipped Numpy arrays generated from Python 3.7. Physics quantities of interest uncertainty distributions are stored in CSV files. A demonstration of the EQUIPS workflow is contained a Python Jupyter notebook, equips.ipynb. Supported by the Laboratory Directed Research and Development program at Sandia National Laboratories, a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA-0003525.



Purdue University, Sandia National Laboratories


Physics, Uncertainty, Image Analysis