Data-efficient U-Net for Segmentation of Carbide Microstructures in SEM Images of Steel Alloys

• URL: http://arxiv.org/abs/2511.11485v1
• Date: Fri, 14 Nov 2025 17:01:02 GMT
• Title: Data-efficient U-Net for Segmentation of Carbide Microstructures in SEM Images of Steel Alloys
• Authors: Alinda Ezgi Gerçek, Till Korten, Paul Chekhonin, Maleeha Hassan, Peter Steinbach,
• Abstract summary: We present a data-efficient segmentation pipeline using a lightweight U-Net (30.7M parameters) trained on just textbf10 annotated scanning electron microscopy images.<n>Despite limited data, our model a textbfDice-Srensen achieves coefficient of 0.98, significantly outperforming the state-of-the-art in the field of metallurgy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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