Automated image segmentation for accelerated nanoparticle characterization
Abstract Recent developments in materials science have made it possible to synthesize millions of individual nanoparticles on a chip. However, many steps in the characterization process still require extensive human input. To address this challenge, we present an automated image processing pipeline...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-01337-z |
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| author | Alexandra L. Day Carolin B. Wahl Roberto dos Reis Wei-keng Liao Youjia Li Muhammed Nur Talha Kilic Chad A. Mirkin Vinayak P. Dravid Alok Choudhary Ankit Agrawal |
| author_facet | Alexandra L. Day Carolin B. Wahl Roberto dos Reis Wei-keng Liao Youjia Li Muhammed Nur Talha Kilic Chad A. Mirkin Vinayak P. Dravid Alok Choudhary Ankit Agrawal |
| author_sort | Alexandra L. Day |
| collection | DOAJ |
| description | Abstract Recent developments in materials science have made it possible to synthesize millions of individual nanoparticles on a chip. However, many steps in the characterization process still require extensive human input. To address this challenge, we present an automated image processing pipeline that optimizes high-throughput nanoparticle characterization using intelligent image segmentation and coordinate generation. The proposed method can rapidly analyze each image and return optimized acquisition coordinates suitable for multiple analytical STEM techniques, including 4D-STEM, EELS, and EDS. The pipeline employs computer vision and unsupervised learning to remove the image background, segment the particle into areas of interest, and generate acquisition coordinates. This approach eliminates the need for uniform grid sampling, focusing data collection on regions of interest. We validated our approach using a diverse dataset of over 900 high-resolution grayscale nanoparticle images, achieving a 96.0% success rate based on expert-validated criteria. Using established 4D-STEM acquisition times as a baseline, our method demonstrates a 25.0 to 29.1-fold reduction in total processing time. By automating this crucial preprocessing step and optimizing data acquisition, our pipeline significantly accelerates materials characterization workflows while reducing unnecessary data collection. |
| format | Article |
| id | doaj-art-9441ea8927c344ceb2da23945179b865 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9441ea8927c344ceb2da23945179b8652025-08-20T01:51:30ZengNature PortfolioScientific Reports2045-23222025-05-0115111310.1038/s41598-025-01337-zAutomated image segmentation for accelerated nanoparticle characterizationAlexandra L. Day0Carolin B. Wahl1Roberto dos Reis2Wei-keng Liao3Youjia Li4Muhammed Nur Talha Kilic5Chad A. Mirkin6Vinayak P. Dravid7Alok Choudhary8Ankit Agrawal9Department of Electrical and Computer Engineering, Northwestern UniversityDepartment of Materials Science and Engineering, Northwestern UniversityDepartment of Materials Science and Engineering, Northwestern UniversityDepartment of Electrical and Computer Engineering, Northwestern UniversityDepartment of Electrical and Computer Engineering, Northwestern UniversityDepartment of Computer Science, Northwestern UniversityDepartment of Materials Science and Engineering, Northwestern UniversityDepartment of Materials Science and Engineering, Northwestern UniversityDepartment of Electrical and Computer Engineering, Northwestern UniversityDepartment of Electrical and Computer Engineering, Northwestern UniversityAbstract Recent developments in materials science have made it possible to synthesize millions of individual nanoparticles on a chip. However, many steps in the characterization process still require extensive human input. To address this challenge, we present an automated image processing pipeline that optimizes high-throughput nanoparticle characterization using intelligent image segmentation and coordinate generation. The proposed method can rapidly analyze each image and return optimized acquisition coordinates suitable for multiple analytical STEM techniques, including 4D-STEM, EELS, and EDS. The pipeline employs computer vision and unsupervised learning to remove the image background, segment the particle into areas of interest, and generate acquisition coordinates. This approach eliminates the need for uniform grid sampling, focusing data collection on regions of interest. We validated our approach using a diverse dataset of over 900 high-resolution grayscale nanoparticle images, achieving a 96.0% success rate based on expert-validated criteria. Using established 4D-STEM acquisition times as a baseline, our method demonstrates a 25.0 to 29.1-fold reduction in total processing time. By automating this crucial preprocessing step and optimizing data acquisition, our pipeline significantly accelerates materials characterization workflows while reducing unnecessary data collection.https://doi.org/10.1038/s41598-025-01337-z |
| spellingShingle | Alexandra L. Day Carolin B. Wahl Roberto dos Reis Wei-keng Liao Youjia Li Muhammed Nur Talha Kilic Chad A. Mirkin Vinayak P. Dravid Alok Choudhary Ankit Agrawal Automated image segmentation for accelerated nanoparticle characterization Scientific Reports |
| title | Automated image segmentation for accelerated nanoparticle characterization |
| title_full | Automated image segmentation for accelerated nanoparticle characterization |
| title_fullStr | Automated image segmentation for accelerated nanoparticle characterization |
| title_full_unstemmed | Automated image segmentation for accelerated nanoparticle characterization |
| title_short | Automated image segmentation for accelerated nanoparticle characterization |
| title_sort | automated image segmentation for accelerated nanoparticle characterization |
| url | https://doi.org/10.1038/s41598-025-01337-z |
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