Pre-trained artificial intelligence-aided analysis of nanoparticles using the segment anything model
Abstract Complex structures can be understood as compositions of smaller, more basic elements. The characterization of these structures requires an analysis of their constituents and their spatial configuration. Examples can be found in systems as diverse as galaxies, alloys, living tissues, cells,...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-86327-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832594794945183744 |
|---|---|
| author | Gabriel A. A. Monteiro Bruno A. A. Monteiro Jefersson A. dos Santos Alexander Wittemann |
| author_facet | Gabriel A. A. Monteiro Bruno A. A. Monteiro Jefersson A. dos Santos Alexander Wittemann |
| author_sort | Gabriel A. A. Monteiro |
| collection | DOAJ |
| description | Abstract Complex structures can be understood as compositions of smaller, more basic elements. The characterization of these structures requires an analysis of their constituents and their spatial configuration. Examples can be found in systems as diverse as galaxies, alloys, living tissues, cells, and even nanoparticles. In the latter field, the most challenging examples are those of subdivided particles and particle-based materials, due to the close proximity of their constituents. The characterization of such nanostructured materials is typically conducted through the utilization of micrographs. Despite the importance of micrograph analysis, the extraction of quantitative data is often constrained. The presented effort demonstrates the morphological characterization of subdivided particles utilizing a pre-trained artificial intelligence model. The results are validated using three types of nanoparticles: nanospheres, dumbbells, and trimers. The automated segmentation of whole particles, as well as their individual subdivisions, is investigated using the Segment Anything Model, which is based on a pre-trained neural network. The subdivisions of the particles are organized into sets, which presents a novel approach in this field. These sets collate data derived from a large ensemble of specific particle domains indicating to which particle each subdomain belongs. The arrangement of subdivisions into sets to characterize complex nanoparticles expands the information gathered from microscopy analysis. The presented method, which employs a pre-trained deep learning model, outperforms traditional techniques by circumventing systemic errors and human bias. It can effectively automate the analysis of particles, thereby providing more accurate and efficient results. |
| format | Article |
| id | doaj-art-d130e17fd6a642abad932405bb143377 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d130e17fd6a642abad932405bb1433772025-01-19T12:21:58ZengNature PortfolioScientific Reports2045-23222025-01-0115111210.1038/s41598-025-86327-xPre-trained artificial intelligence-aided analysis of nanoparticles using the segment anything modelGabriel A. A. Monteiro0Bruno A. A. Monteiro1Jefersson A. dos Santos2Alexander Wittemann3Colloid Chemistry, Department of Chemistry, University of KonstanzPattern Recognition and Earth Observation Laboratory, Department of Computer Science, Federal University of Minas GeraisPattern Recognition and Earth Observation Laboratory, Department of Computer Science, Federal University of Minas GeraisColloid Chemistry, Department of Chemistry, University of KonstanzAbstract Complex structures can be understood as compositions of smaller, more basic elements. The characterization of these structures requires an analysis of their constituents and their spatial configuration. Examples can be found in systems as diverse as galaxies, alloys, living tissues, cells, and even nanoparticles. In the latter field, the most challenging examples are those of subdivided particles and particle-based materials, due to the close proximity of their constituents. The characterization of such nanostructured materials is typically conducted through the utilization of micrographs. Despite the importance of micrograph analysis, the extraction of quantitative data is often constrained. The presented effort demonstrates the morphological characterization of subdivided particles utilizing a pre-trained artificial intelligence model. The results are validated using three types of nanoparticles: nanospheres, dumbbells, and trimers. The automated segmentation of whole particles, as well as their individual subdivisions, is investigated using the Segment Anything Model, which is based on a pre-trained neural network. The subdivisions of the particles are organized into sets, which presents a novel approach in this field. These sets collate data derived from a large ensemble of specific particle domains indicating to which particle each subdomain belongs. The arrangement of subdivisions into sets to characterize complex nanoparticles expands the information gathered from microscopy analysis. The presented method, which employs a pre-trained deep learning model, outperforms traditional techniques by circumventing systemic errors and human bias. It can effectively automate the analysis of particles, thereby providing more accurate and efficient results.https://doi.org/10.1038/s41598-025-86327-xMicroscopyImage processingNanoparticlesArtificial intelligenceSegment anything modelParticle morphology |
| spellingShingle | Gabriel A. A. Monteiro Bruno A. A. Monteiro Jefersson A. dos Santos Alexander Wittemann Pre-trained artificial intelligence-aided analysis of nanoparticles using the segment anything model Scientific Reports Microscopy Image processing Nanoparticles Artificial intelligence Segment anything model Particle morphology |
| title | Pre-trained artificial intelligence-aided analysis of nanoparticles using the segment anything model |
| title_full | Pre-trained artificial intelligence-aided analysis of nanoparticles using the segment anything model |
| title_fullStr | Pre-trained artificial intelligence-aided analysis of nanoparticles using the segment anything model |
| title_full_unstemmed | Pre-trained artificial intelligence-aided analysis of nanoparticles using the segment anything model |
| title_short | Pre-trained artificial intelligence-aided analysis of nanoparticles using the segment anything model |
| title_sort | pre trained artificial intelligence aided analysis of nanoparticles using the segment anything model |
| topic | Microscopy Image processing Nanoparticles Artificial intelligence Segment anything model Particle morphology |
| url | https://doi.org/10.1038/s41598-025-86327-x |
| work_keys_str_mv | AT gabrielaamonteiro pretrainedartificialintelligenceaidedanalysisofnanoparticlesusingthesegmentanythingmodel AT brunoaamonteiro pretrainedartificialintelligenceaidedanalysisofnanoparticlesusingthesegmentanythingmodel AT jeferssonadossantos pretrainedartificialintelligenceaidedanalysisofnanoparticlesusingthesegmentanythingmodel AT alexanderwittemann pretrainedartificialintelligenceaidedanalysisofnanoparticlesusingthesegmentanythingmodel |