Timothy P. Day, Khaled Mosharraf Mukut, Luke Klacik, Ryan O’Donnell, James Wasilewski, Somesh P. Roy
{"title":"SAGE: A machine learning model for primary particle segmentation in TEM images of soot aggregates","authors":"Timothy P. Day, Khaled Mosharraf Mukut, Luke Klacik, Ryan O’Donnell, James Wasilewski, Somesh P. Roy","doi":"10.1016/j.proci.2025.105821","DOIUrl":null,"url":null,"abstract":"<div><div>Accurate characterization of the morphology of soot is essential for our understanding and better modeling of the physical and chemical properties of soot. The morphological characteristics of soot are traditionally explored experimentally via Transmission Electron Microscopy (TEM), usually by investigating the images via manual segmentation, which is highly labor intensive. To improve this process, a novel model for the automatic segmentation of primary particles in TEM images of soot is presented in this work. The goal of the model is to identify and isolate each primary particle from a TEM image of a soot aggregate. The model, titled Soot Aggregate Geometry Extraction (SAGE) employs a two-stage training process using a convolutional neural network: an initial training on synthetically-generated TEM images followed by a refinement training by using manually segmented real TEM images. The model was tested against a dataset of real TEM images that included images from sources different from the training data (i.e., different instruments and different researchers). When tested against this real TEM image dataset of soot, SAGE shows good performance with an F<span><math><msub><mrow></mrow><mrow><mn>1</mn></mrow></msub></math></span> score of 67.7%, indicating its ability to correctly identify primary particles while achieving a balanced trade off between missing true particles and detecting false ones. SAGE is able to detect more primary particles with better shape and size alignments with the ground truth data than traditional methods such as circular Hough transform or Euclidean distance mapping methods, leading to a much higher mean Intersection over Union score of 62.2%. Unlike most existing approaches that produce circular segmentations and require image-by-image tuning, SAGE effectively captures irregular particle boundaries without additional adjustments. The particle size distribution obtained from SAGE matches well with the ground truth. The median errors of predictions obtained from SAGE fall below 5% and 1%, respectively, for radius of gyration and fractal dimension of particles.</div></div>","PeriodicalId":408,"journal":{"name":"Proceedings of the Combustion Institute","volume":"41 ","pages":"Article 105821"},"PeriodicalIF":5.2000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Combustion Institute","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1540748925000355","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
Accurate characterization of the morphology of soot is essential for our understanding and better modeling of the physical and chemical properties of soot. The morphological characteristics of soot are traditionally explored experimentally via Transmission Electron Microscopy (TEM), usually by investigating the images via manual segmentation, which is highly labor intensive. To improve this process, a novel model for the automatic segmentation of primary particles in TEM images of soot is presented in this work. The goal of the model is to identify and isolate each primary particle from a TEM image of a soot aggregate. The model, titled Soot Aggregate Geometry Extraction (SAGE) employs a two-stage training process using a convolutional neural network: an initial training on synthetically-generated TEM images followed by a refinement training by using manually segmented real TEM images. The model was tested against a dataset of real TEM images that included images from sources different from the training data (i.e., different instruments and different researchers). When tested against this real TEM image dataset of soot, SAGE shows good performance with an F score of 67.7%, indicating its ability to correctly identify primary particles while achieving a balanced trade off between missing true particles and detecting false ones. SAGE is able to detect more primary particles with better shape and size alignments with the ground truth data than traditional methods such as circular Hough transform or Euclidean distance mapping methods, leading to a much higher mean Intersection over Union score of 62.2%. Unlike most existing approaches that produce circular segmentations and require image-by-image tuning, SAGE effectively captures irregular particle boundaries without additional adjustments. The particle size distribution obtained from SAGE matches well with the ground truth. The median errors of predictions obtained from SAGE fall below 5% and 1%, respectively, for radius of gyration and fractal dimension of particles.
期刊介绍:
The Proceedings of the Combustion Institute contains forefront contributions in fundamentals and applications of combustion science. For more than 50 years, the Combustion Institute has served as the peak international society for dissemination of scientific and technical research in the combustion field. In addition to author submissions, the Proceedings of the Combustion Institute includes the Institute''s prestigious invited strategic and topical reviews that represent indispensable resources for emergent research in the field. All papers are subjected to rigorous peer review.
Research papers and invited topical reviews; Reaction Kinetics; Soot, PAH, and other large molecules; Diagnostics; Laminar Flames; Turbulent Flames; Heterogeneous Combustion; Spray and Droplet Combustion; Detonations, Explosions & Supersonic Combustion; Fire Research; Stationary Combustion Systems; IC Engine and Gas Turbine Combustion; New Technology Concepts
The electronic version of Proceedings of the Combustion Institute contains supplemental material such as reaction mechanisms, illustrating movies, and other data.