{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/high-throughput-quantitative-metallography","title":"High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel","arxiv_id":"1805.08693","date":"2018-05-04","proceeding":null,"authors":["Brian L. DeCost","Bo Lei","Toby Francis","Elizabeth A. Holm"],"abstract":"We apply a deep convolutional neural network segmentation model to enable\nnovel automated microstructure segmentation applications for complex\nmicrostructures typically evaluated manually and subjectively. We explore two\nmicrostructure segmentation tasks in an openly-available ultrahigh carbon steel\nmicrostructure dataset: segmenting cementite particles in the spheroidized\nmatrix, and segmenting larger fields of view featuring grain boundary carbide,\nspheroidized particle matrix, particle-free grain boundary denuded zone, and\nWidmanst\\\"atten cementite. We also demonstrate how to combine these data-driven\nmicrostructure segmentation models to obtain empirical cementite particle size\nand denuded zone width distributions from more complex micrographs containing\nmultiple microconstituents. The full annotated dataset is available on\nmaterialsdata.nist.gov (https://materialsdata.nist.gov/handle/11256/964).","url_abs":"http://arxiv.org/abs/1805.08693v2","url_pdf":"http://arxiv.org/pdf/1805.08693v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"high-throughput-quantitative-metallography","repo_url":"https://github.com/bdecost/pixelnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"high-throughput-quantitative-metallography","repo_url":"https://github.com/bdecost/uhcs-segment","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"high-throughput-quantitative-metallography","repo_url":"https://github.com/leibo-cmu/MatSeg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}