Data-efficient U-Net for Segmentation of Carbide Microstructures in SEM Images of Steel Alloys
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908653625278464 |
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| author | Gerçek, Alinda Ezgi Korten, Till Chekhonin, Paul Hassan, Maleeha Steinbach, Peter |
| author_facet | Gerçek, Alinda Ezgi Korten, Till Chekhonin, Paul Hassan, Maleeha Steinbach, Peter |
| contents | Understanding reactor-pressure-vessel steel microstructure is crucial for predicting mechanical properties, as carbide precipitates both strengthen the alloy and can initiate cracks. In scanning electron microscopy images, gray-value overlap between carbides and matrix makes simple thresholding ineffective. We present a data-efficient segmentation pipeline using a lightweight U-Net (30.7~M parameters) trained on just \textbf{10 annotated scanning electron microscopy images}. Despite limited data, our model achieves a \textbf{Dice-Sørensen coefficient of 0.98}, significantly outperforming the state-of-the-art in the field of metallurgy (classical image analysis: 0.85), while reducing annotation effort by one order of magnitude compared to the state-of-the-art data efficient segmentation model. This approach enables rapid, automated carbide quantification for alloy design and generalizes to other steel types, demonstrating the potential of data-efficient deep learning in reactor-pressure-vessel steel analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11485 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-efficient U-Net for Segmentation of Carbide Microstructures in SEM Images of Steel Alloys Gerçek, Alinda Ezgi Korten, Till Chekhonin, Paul Hassan, Maleeha Steinbach, Peter Machine Learning Materials Science I.4.6 Understanding reactor-pressure-vessel steel microstructure is crucial for predicting mechanical properties, as carbide precipitates both strengthen the alloy and can initiate cracks. In scanning electron microscopy images, gray-value overlap between carbides and matrix makes simple thresholding ineffective. We present a data-efficient segmentation pipeline using a lightweight U-Net (30.7~M parameters) trained on just \textbf{10 annotated scanning electron microscopy images}. Despite limited data, our model achieves a \textbf{Dice-Sørensen coefficient of 0.98}, significantly outperforming the state-of-the-art in the field of metallurgy (classical image analysis: 0.85), while reducing annotation effort by one order of magnitude compared to the state-of-the-art data efficient segmentation model. This approach enables rapid, automated carbide quantification for alloy design and generalizes to other steel types, demonstrating the potential of data-efficient deep learning in reactor-pressure-vessel steel analysis. |
| title | Data-efficient U-Net for Segmentation of Carbide Microstructures in SEM Images of Steel Alloys |
| topic | Machine Learning Materials Science I.4.6 |
| url | https://arxiv.org/abs/2511.11485 |