Počet záznamů: 1
A Comparison of Image Analysis Tools for Segmentation on SEM Micrographs - Zeiss ZEN Intellesis vs. Thermofisher AVIZO
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SYSNO ASEP 0575302 Druh ASEP A - Abstrakt Zařazení RIV Záznam nebyl označen do RIV Zařazení RIV Není vybrán druh dokumentu Název A Comparison of Image Analysis Tools for Segmentation on SEM Micrographs - Zeiss ZEN Intellesis vs. Thermofisher AVIZO Tvůrce(i) Jozefovič, Patrik (UPT-D) ORCID, RID, SAI
Ambrož, Ondřej (UPT-D) ORCID, RID, SAI
Čermák, Jan (UPT-D)
Man, Jiří (UFM-A) RID, ORCID
Mikmeková, Šárka (UPT-D) RID, SAI, ORCIDZdroj.dok. Microscopy and Microanalysis. - : Cambridge University Press - ISSN 1431-9276
Roč. 29, S1 (2023), s. 1889-1891Poč.str. 3 s. Forma vydání Tištěná - P Akce Microscopy & Microanalysis 2023 Datum konání 23.07.2023 - 27.07.2023 Místo konání Minneapolis Země US - Spojené státy americké Typ akce WRD Jazyk dok. eng - angličtina Země vyd. GB - Velká Británie Klíč. slova machine learning ; image analysis ; segmentation ; metalography ; scanning electron microscopy Vědní obor RIV JG - Hutnictví, kovové materiály Obor OECD Materials engineering Institucionální podpora UPT-D - RVO:68081731 ; UFM-A - RVO:68081723 EID SCOPUS 85168609823 DOI https://doi.org/10.1093/micmic/ozad067.975 Anotace AISI301LN, a low-carbon variant of austenitic stainless steel renowned for its superior corrosion resistance, strength, and versatility, finds widespread applications across industries. However, its susceptibility to martensitic transformation during plastic deformation poses challenges to its mechanical properties. To address this, in our study we are focusing on two key aspects. Firstly, we aim to develop accelerated imaging methods for detecting martensite in the microstructure, to unlock the possibility of understanding the behaviour of the AISI301LN, as well as detecting and mapping the proportion of martensite phase in microstructure. Secondly, we explore automated image analysis techniques for precise segmentation of phases in scanning electron microscope (SEM) micrographs. We assess the performance of commercial solutions such as Thermo Fisher Amira-Avizo and ZEISS ZEN Intellesis, comparing them with custom-trained neural network models based on established architectures for image segmentation. Our research not only contributes to a deeper understanding of AISI301LN behavior but also evaluates the effectiveness of machine learning tools in enhancing its properties. Pracoviště Ústav přístrojové techniky Kontakt Martina Šillerová, sillerova@ISIBrno.Cz, Tel.: 541 514 178 Rok sběru 2024 Elektronická adresa https://academic.oup.com/mam/article/29/Supplement_1/1889/7228906
Počet záznamů: 1