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

arXiv: https://arxiv.org/abs/2511.11485 · HTML: https://mlsanity.com/paper/2511.11485/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2025.

## Abstract (arXiv)

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 10 annotated scanning electron microscopy images. Despite limited data, our model achieves a textbfDice-Sorensen 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.