一种有效的U-10 Zr环形燃料裂变气泡实例分割方法
An Efficient Instance Segmentation Approach for Extracting Fission Gas Bubbles on U-10Zr Annular Fuel
摘要：
铀-10锆基核燃料是下一代钠冷快堆的主要候选燃料。然而，需要更先进的表征和分析，以形成对燃料性能的基本理解，并使U-10 Zr燃料有资格用于商业用途。镧系元素沿燃料截面从热燃料中心向冷包壳表面的迁移是影响燃料性能的关键因素之一。在先进的环形U-10 Zr燃料中，镧系元素以裂变气泡的形式存在。由于缺乏注释数据，现有文献利用多阈值方法来分离气泡并计算环形燃料上的气泡统计。然而，多阈值方法不能在具有不同质量和对比度的图像上实现鲁棒性能，并且不能区分不同的气泡。本文提出了一种混合框架，有效的气泡分割。我们开发了一个气泡标注工具，并从24幅图像中生成了第一个包含3000多个气泡的裂变气泡数据集。设计了一个集成U-Net和ResNet的多任务深度学习网络来完成实例级气泡分割。结合分割结果和图像处理步骤，在非常有限的注释数据下实现了超过90%的最佳召回率。我们的模型表现出突出的改进，通过比较以前提出的阈值方法。所提出的方法有希望产生一个更准确的定量分析的裂变气泡的U-10 Zr环形燃料。研究结果将有助于识别U-10 Zr环形燃料中的镧系元素气泡，并最终建立U-10 Zr环形燃料热级配与镧系元素迁移之间的关系。此外，深度学习模型也适用于其他类似材料的微观结构分割任务。
英文摘要：
U-10Zr-based nuclear fuel is pursued as a primary candidate for next-generation sodium-cooled fast reactors. However, more advanced characterization and analysis are needed to form a fundamental understating of the fuel performance, and make U-10Zr fuel qualify for commercial use. The movement of lanthanides across the fuel section from the hot fuel center to the cool cladding surface is one of the key factors to affect fuel performance. In the advanced annular U-10Zr fuel, the lanthanides present as fission gas bubbles. Due to a lack of annotated data, existing literature utilized a multiple-threshold method to separate the bubbles and calculate bubble statistics on an annular fuel. However, the multiple-threshold method cannot achieve robust performance on images with different qualities and contrasts, and cannot distinguish different bubbles. This paper proposes a hybrid framework for efficient bubble segmentation. We develop a bubble annotation tool and generate the first fission gas bubble dataset with more than 3000 bubbles from 24 images. A multi-task deep learning network integrating U-Net and ResNet is designed to accomplish instance-level bubble segmentation. Combining the segmentation results and image processing step achieves the best recall ratio of more than 90% with very limited annotated data. Our model shows outstanding improvement by comparing the previously proposed thresholding method. The proposed method has promising to generate a more accurate quantitative analysis of fission gas bubbles on U-10Zr annular fuels. The results will contribute to identifying the bubbles with lanthanides and finally build the relationship between the thermal gradation and lanthanides movements of U-10Zr annular fuels. Mover, the deep learning model is applicable to other similar material micro-structure segmentation tasks.
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