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A Comparison of Image Analysis Tools for Segmentation on SEM Micrographs - Zeiss ZEN Intellesis vs. Thermofisher AVIZO
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SYSNO ASEP 0575302 Document Type A - Abstract R&D Document Type The record was not marked in the RIV R&D Document Type Není vybrán druh dokumentu Title A Comparison of Image Analysis Tools for Segmentation on SEM Micrographs - Zeiss ZEN Intellesis vs. Thermofisher AVIZO Author(s) Jozefovič, Patrik (UPT-D)
Ambrož, Ondřej (UPT-D) ORCID, RID, SAI
Čermák, Jan (UPT-D)
Man, Jiří (UFM-A) RID, ORCID
Mikmeková, Šárka (UPT-D) RID, SAI, ORCIDSource Title Microscopy and Microanalysis. - : Cambridge University Press - ISSN 1431-9276
Roč. 29, S1 (2023), s. 1889-1891Number of pages 3 s. Publication form Print - P Action Microscopy & Microanalysis 2023 Event date 23.07.2023 - 27.07.2023 VEvent location Minneapolis Country US - United States Event type WRD Language eng - English Country GB - United Kingdom Keywords machine learning ; image analysis ; segmentation ; metalography ; scanning electron microscopy Subject RIV JG - Metallurgy OECD category Materials engineering Institutional support UPT-D - RVO:68081731 ; UFM-A - RVO:68081723 EID SCOPUS 85168609823 DOI 10.1093/micmic/ozad067.975 Annotation 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. Workplace Institute of Scientific Instruments Contact Martina Šillerová, sillerova@ISIBrno.Cz, Tel.: 541 514 178 Year of Publishing 2024 Electronic address https://academic.oup.com/mam/article/29/Supplement_1/1889/7228906
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