Data-driven simulations for training AI-based segmentation of neutron images
Neutron interferometry uniquely combines neutron imaging and scattering methods to enable characterization of multiple length scales from 1 nm to 10 µm. However, building, operating, and using such neutron imaging instruments poses constraints on the acquisition time and on the number of measured im...
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| Published in: | Scientific reports Vol. 14; no. 1; p. 6614 |
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| Main Authors: | , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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19-03-2024
Nature Publishing Group Nature Portfolio |
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| Abstract | Neutron interferometry uniquely combines neutron imaging and scattering methods to enable characterization of multiple length scales from 1 nm to 10 µm. However, building, operating, and using such neutron imaging instruments poses constraints on the acquisition time and on the number of measured images per sample. Experiment time-constraints yield small quantities of measured images that are insufficient for automating image analyses using supervised artificial intelligence (AI) models. One approach alleviates this problem by supplementing annotated measured images with synthetic images. To this end, we create a data-driven simulation framework that supplements training data beyond typical data-driven augmentations by leveraging statistical intensity models, such as the Johnson family of probability density functions (PDFs). We follow the simulation framework steps for an image segmentation task including Estimate PDFs
→
Validate PDFs
→
Design Image Masks
→
Generate Intensities
→
Train AI Model for Segmentation. Our goal is to minimize the manual labor needed to execute the steps and maximize our confidence in simulations and segmentation accuracy. We report results for a set of nine known materials (calibration phantoms) that were imaged using a neutron interferometer acquiring four-dimensional images and segmented by AI models trained with synthetic and measured images and their masks. |
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| AbstractList | Neutron interferometry uniquely combines neutron imaging and scattering methods to enable characterization of multiple length scales from 1 nm to 10 µm. However, building, operating, and using such neutron imaging instruments poses constraints on the acquisition time and on the number of measured images per sample. Experiment time-constraints yield small quantities of measured images that are insufficient for automating image analyses using supervised artificial intelligence (AI) models. One approach alleviates this problem by supplementing annotated measured images with synthetic images. To this end, we create a data-driven simulation framework that supplements training data beyond typical data-driven augmentations by leveraging statistical intensity models, such as the Johnson family of probability density functions (PDFs). We follow the simulation framework steps for an image segmentation task including Estimate PDFs
Validate PDFs
Design Image Masks
Generate Intensities
Train AI Model for Segmentation. Our goal is to minimize the manual labor needed to execute the steps and maximize our confidence in simulations and segmentation accuracy. We report results for a set of nine known materials (calibration phantoms) that were imaged using a neutron interferometer acquiring four-dimensional images and segmented by AI models trained with synthetic and measured images and their masks. Neutron interferometry uniquely combines neutron imaging and scattering methods to enable characterization of multiple length scales from 1 nm to 10 µm. However, building, operating, and using such neutron imaging instruments poses constraints on the acquisition time and on the number of measured images per sample. Experiment time-constraints yield small quantities of measured images that are insufficient for automating image analyses using supervised artificial intelligence (AI) models. One approach alleviates this problem by supplementing annotated measured images with synthetic images. To this end, we create a data-driven simulation framework that supplements training data beyond typical data-driven augmentations by leveraging statistical intensity models, such as the Johnson family of probability density functions (PDFs). We follow the simulation framework steps for an image segmentation task including Estimate PDFs → Validate PDFs → Design Image Masks → Generate Intensities → Train AI Model for Segmentation. Our goal is to minimize the manual labor needed to execute the steps and maximize our confidence in simulations and segmentation accuracy. We report results for a set of nine known materials (calibration phantoms) that were imaged using a neutron interferometer acquiring four-dimensional images and segmented by AI models trained with synthetic and measured images and their masks. Neutron interferometry uniquely combines neutron imaging and scattering methods to enable characterization of multiple length scales from 1 nm to 10 µm. However, building, operating, and using such neutron imaging instruments poses constraints on the acquisition time and on the number of measured images per sample. Experiment time-constraints yield small quantities of measured images that are insufficient for automating image analyses using supervised artificial intelligence (AI) models. One approach alleviates this problem by supplementing annotated measured images with synthetic images. To this end, we create a data-driven simulation framework that supplements training data beyond typical data-driven augmentations by leveraging statistical intensity models, such as the Johnson family of probability density functions (PDFs). We follow the simulation framework steps for an image segmentation task including Estimate PDFs $$\,\rightarrow \,$$ → Validate PDFs $$\,\rightarrow \,$$ → Design Image Masks $$\,\rightarrow \,$$ → Generate Intensities $$\,\rightarrow \,$$ → Train AI Model for Segmentation. Our goal is to minimize the manual labor needed to execute the steps and maximize our confidence in simulations and segmentation accuracy. We report results for a set of nine known materials (calibration phantoms) that were imaged using a neutron interferometer acquiring four-dimensional images and segmented by AI models trained with synthetic and measured images and their masks. Neutron interferometry uniquely combines neutron imaging and scattering methods to enable characterization of multiple length scales from 1 nm to 10 µm. However, building, operating, and using such neutron imaging instruments poses constraints on the acquisition time and on the number of measured images per sample. Experiment time-constraints yield small quantities of measured images that are insufficient for automating image analyses using supervised artificial intelligence (AI) models. One approach alleviates this problem by supplementing annotated measured images with synthetic images. To this end, we create a data-driven simulation framework that supplements training data beyond typical data-driven augmentations by leveraging statistical intensity models, such as the Johnson family of probability density functions (PDFs). We follow the simulation framework steps for an image segmentation task including Estimate PDFs → Validate PDFs → Design Image Masks → Generate Intensities → Train AI Model for Segmentation. Our goal is to minimize the manual labor needed to execute the steps and maximize our confidence in simulations and segmentation accuracy. We report results for a set of nine known materials (calibration phantoms) that were imaged using a neutron interferometer acquiring four-dimensional images and segmented by AI models trained with synthetic and measured images and their masks. Neutron interferometry uniquely combines neutron imaging and scattering methods to enable characterization of multiple length scales from 1 nm to 10 µm. However, building, operating, and using such neutron imaging instruments poses constraints on the acquisition time and on the number of measured images per sample. Experiment time-constraints yield small quantities of measured images that are insufficient for automating image analyses using supervised artificial intelligence (AI) models. One approach alleviates this problem by supplementing annotated measured images with synthetic images. To this end, we create a data-driven simulation framework that supplements training data beyond typical data-driven augmentations by leveraging statistical intensity models, such as the Johnson family of probability density functions (PDFs). We follow the simulation framework steps for an image segmentation task including Estimate PDFs \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\,\rightarrow \,$$\end{document} → Validate PDFs \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\,\rightarrow \,$$\end{document} → Design Image Masks \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\,\rightarrow \,$$\end{document} → Generate Intensities \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\,\rightarrow \,$$\end{document} → Train AI Model for Segmentation. Our goal is to minimize the manual labor needed to execute the steps and maximize our confidence in simulations and segmentation accuracy. We report results for a set of nine known materials (calibration phantoms) that were imaged using a neutron interferometer acquiring four-dimensional images and segmented by AI models trained with synthetic and measured images and their masks. Abstract Neutron interferometry uniquely combines neutron imaging and scattering methods to enable characterization of multiple length scales from 1 nm to 10 µm. However, building, operating, and using such neutron imaging instruments poses constraints on the acquisition time and on the number of measured images per sample. Experiment time-constraints yield small quantities of measured images that are insufficient for automating image analyses using supervised artificial intelligence (AI) models. One approach alleviates this problem by supplementing annotated measured images with synthetic images. To this end, we create a data-driven simulation framework that supplements training data beyond typical data-driven augmentations by leveraging statistical intensity models, such as the Johnson family of probability density functions (PDFs). We follow the simulation framework steps for an image segmentation task including Estimate PDFs $$\,\rightarrow \,$$ → Validate PDFs $$\,\rightarrow \,$$ → Design Image Masks $$\,\rightarrow \,$$ → Generate Intensities $$\,\rightarrow \,$$ → Train AI Model for Segmentation. Our goal is to minimize the manual labor needed to execute the steps and maximize our confidence in simulations and segmentation accuracy. We report results for a set of nine known materials (calibration phantoms) that were imaged using a neutron interferometer acquiring four-dimensional images and segmented by AI models trained with synthetic and measured images and their masks. |
| ArticleNumber | 6614 |
| Author | Klimov, Nikolai N. Daugherty, M. Cyrus Hussey, Daniel S. Huber, Michael G. Murphy, Ryan P. Sathe, Pushkar S. Kienzle, Paul A. LaManna, Jacob M. Robinson, Sarah M. Wolf, Caitlyn M. Bajcsy, Peter Jacobson, David L. Weigandt, Katie M. Kim, Youngju |
| Author_xml | – sequence: 1 givenname: Pushkar S. surname: Sathe fullname: Sathe, Pushkar S. organization: Information Technology Laboratory, NIST – sequence: 2 givenname: Caitlyn M. surname: Wolf fullname: Wolf, Caitlyn M. organization: NIST Center for Neutron Research – sequence: 3 givenname: Youngju surname: Kim fullname: Kim, Youngju organization: Physical Measurement Laboratory, Department of Chemistry and Biochemistry, University of Maryland – sequence: 4 givenname: Sarah M. surname: Robinson fullname: Robinson, Sarah M. organization: Physical Measurement Laboratory – sequence: 5 givenname: M. Cyrus surname: Daugherty fullname: Daugherty, M. Cyrus organization: Physical Measurement Laboratory – sequence: 6 givenname: Ryan P. surname: Murphy fullname: Murphy, Ryan P. organization: NIST Center for Neutron Research – sequence: 7 givenname: Jacob M. surname: LaManna fullname: LaManna, Jacob M. organization: Physical Measurement Laboratory – sequence: 8 givenname: Michael G. surname: Huber fullname: Huber, Michael G. organization: Physical Measurement Laboratory – sequence: 9 givenname: David L. surname: Jacobson fullname: Jacobson, David L. organization: Physical Measurement Laboratory – sequence: 10 givenname: Paul A. surname: Kienzle fullname: Kienzle, Paul A. organization: NIST Center for Neutron Research – sequence: 11 givenname: Katie M. surname: Weigandt fullname: Weigandt, Katie M. organization: NIST Center for Neutron Research – sequence: 12 givenname: Nikolai N. surname: Klimov fullname: Klimov, Nikolai N. organization: Physical Measurement Laboratory – sequence: 13 givenname: Daniel S. surname: Hussey fullname: Hussey, Daniel S. organization: Physical Measurement Laboratory – sequence: 14 givenname: Peter surname: Bajcsy fullname: Bajcsy, Peter email: peter.bajcsy@nist.gov organization: Information Technology Laboratory, NIST |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38503854$$D View this record in MEDLINE/PubMed |
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| Keywords | Data-driven simulation Semantic segmentation Neutron imaging INFER |
| Language | English |
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| SubjectTerms | 639/301/930/12 639/301/930/2735 639/766/930 639/925/930 Artificial intelligence Automation Data-driven simulation Design Humanities and Social Sciences Image processing INFER Interferometry Mathematical models multidisciplinary Neutron imaging Neutrons Science Science (multidisciplinary) Semantic segmentation Simulation Statistical analysis |
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| Title | Data-driven simulations for training AI-based segmentation of neutron images |
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