仅用10张标注图训练轻量U-Net，实现钢中碳化物高精度分割。
Data-efficient U-Net for Segmentation of Carbide Microstructures in SEM Images of Steel Alloys
- 基于极少量标注图像，设计轻量化U-Net模型进行微结构分割。
- Dice系数达0.98，显著优于传统方法（0.85）和现有高效模型。
- 适用于合金研发快速定量分析，适合材料领域数据稀缺场景。
理解反应堆压力容器钢的显微组织对预测力学性能至关重要，因为碳化物析出既强化合金又可能引发裂纹。在扫描电子显微镜图像中，碳化物与基体灰度重叠导致简单阈值法失效。本文提出一种数据高效的分割流程，采用参数量为30.7~M的轻量U-Net，在仅10张标注的SEM图像上进行训练。尽管数据有限，该模型仍取得0.98的Dice-Sørensen系数，显著优于冶金领域现有技术（经典图像分析：0.85），且标注成本较当前最优数据高效分割模型降低一个数量级。该方法可实现合金设计中的碳化物快速自动量化，并推广至其他钢种，展示了数据高效深度学习在反应堆压力容器钢分析中的潜力。
原文摘要 · Abstract (English)
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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