{"ID":2841837,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.11485","arxiv_id":"2511.11485","title":"Data-efficient U-Net for Segmentation of Carbide Microstructures in SEM Images of Steel Alloys","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.","short_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...","url_abs":"https://arxiv.org/abs/2511.11485","url_pdf":"https://arxiv.org/pdf/2511.11485v1","authors":"[\"Alinda Ezgi Gerçek\",\"Till Korten\",\"Paul Chekhonin\",\"Maleeha Hassan\",\"Peter Steinbach\"]","published":"2025-11-14T17:01:02Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cond-mat.mtrl-sci\"]","methods":"[]","has_code":false}