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Credible, Automated Meshing of Images (CAMI)

Roberts, Scott A.; Donohoe, Brendan D.; Martinez, Carianne M.; Krygier, Michael K.; Hernandez-Sanchez, Bernadette A.; Foster, Collin W.; Collins, Lincoln; Greene, Benjamin G.; Noble, David R.; Norris, Chance A.; Potter, Kevin M.; Roberts, Christine C.; Neal, Kyle D.; Bernard, Sylvain R.; Schroeder, Benjamin B.; Trembacki, Bradley; Labonte, Tyler; Sharma, Krish; Ganter, Tyler G.; Jones, Jessica E.; Smith, Matthew D.

Abstract not provided.

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TYPE Presentation YEAR 2021
OSTIOSTI

Investigating Machine Learning Based X-Ray Computed Tomography Reconstruction Methods to Enhance the Accuracy of CT Scans

Martinez, Carianne M.; Korbin, John P.; Potter, Kevin M.; Donahue, Emily D.; Gamet, Jeremy D.; Smith, Matthew D.

Development of new materials and predictive capabilities of component performance hinges on the ability to accurately digitize "as-built" geometries. X-ray computed tomography (CT) offers a non-destructive method of capturing these details but current methodologies are unable to produce the required fidelity for critical component certification. This project focused on discovering the limitations of existing CT reconstruction algorithms and exploring machine learning (ML) methodologies to overcome these limitations. We found that existing CT reconstruction methods are insufficient for Sandia's critical component certification process and that ML algorithms are a viable path forward to improving the quality of CT images.

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TYPE Other Report YEAR 2019
DOIOSTI

Segmentation certainty through uncertainty: Uncertainty-refined binary volumetric segmentation under multifactor domain shift

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Martinez, Carianne M.; Potter, Kevin M.; Smith, Matthew D.; Donahue, Emily D.; Collins, Lincoln; Korbin, John P.; Roberts, Scott A.

Deep learning segmentation models are known to be sensitive to the scale, contrast, and distribution of pixel values when applied to Computed Tomography (CT) images. For material samples, scans are often obtained from a variety of scanning equipment and resolutions resulting in domain shift. The ability of segmentation models to generalize to examples from these shifted domains relies on how well the distribution of the training data represents the overall distribution of the target data. We present a method to overcome the challenges presented by domain shifts. Our results indicate that we can leverage a deep learning model trained on one domain to accurately segment similar materials at different resolutions by refining binary predictions using uncertainty quantification (UQ). We apply this technique to a set of unlabeled CT scans of woven composite materials with clear qualitative improvement of binary segmentations over the original deep learning predictions. In contrast to prior work, our technique enables refined segmentations without the expense of the additional training time and parameters associated with deep learning models used to address domain shift.

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TYPE Conference Poster YEAR 2019
OSTIScopus
19 Results
19 Results