Laboratoire de Mécanique des Solides (LMS), CNRS UMR 7649, Ecole Polytechnique, Institut Polytechnique de Paris, Route de Saclay, 91128 Palaiseau Cedex, France.
Laboratory for Neutron Scattering and Imaging, Paul Scherrer Institut, CH-5232 Villigen-PSI, Switzerland.
J Synchrotron Radiat. 2022 Sep 1;29(Pt 5):1232-1240. doi: 10.1107/S1600577522006816. Epub 2022 Jul 29.
New developments at synchrotron beamlines and the ongoing upgrades of synchrotron facilities allow the possibility to study complex structures with a much better spatial and temporal resolution than ever before. However, the downside is that the data collected are also significantly larger (more than several terabytes) than ever before, and post-processing and analyzing these data is very challenging to perform manually. This issue can be solved by employing automated methods such as machine learning, which show significantly improved performance in data processing and image segmentation than manual methods. In this work, a 3D U-net deep convolutional neural network (DCNN) model with four layers and base-8 characteristic features has been developed to segment precipitates and porosities in synchrotron transmission X-ray micrograms. Transmission X-ray microscopy experiments were conducted on micropillars prepared from additively manufactured 316L steel to evaluate precipitate information. After training the 3D U-net DCNN model, it was used on unseen data and the prediction was compared with manual segmentation. A good agreement was found between both segmentations. An ablation study was performed and revealed that the proposed model showed better statistics than other models with lower numbers of layers and/or characteristic features. The proposed model is able to segment several hundreds of gigabytes of data in a few minutes and could be applied to other materials and tomography techniques. The code and the fitted weights are made available with this paper for any interested researcher to use for their needs (https://github.com/manasvupadhyay/erc-gamma-3D-DCNN).
同步加速器光束线的新发展和同步加速器设施的持续升级使得人们有可能以前所未有的空间和时间分辨率研究复杂结构。然而，不利的一面是，所收集的数据也比以往任何时候都大得多（超过几个太字节），并且手动进行这些数据的后处理和分析非常具有挑战性。这个问题可以通过采用自动化方法来解决，例如机器学习，它在数据处理和图像分割方面的性能明显优于手动方法。在这项工作中，开发了一个具有四个层和基 8 特征的 3D U-net 深度卷积神经网络（DCNN）模型，用于分割同步加速器透射 X 射线显微镜中的析出物和孔隙。对通过增材制造 316L 钢制备的微柱进行透射 X 射线显微镜实验，以评估析出物信息。在训练 3D U-net DCNN 模型后，将其应用于未见数据，并将预测与手动分割进行比较。发现两种分割之间具有良好的一致性。进行了消融研究，结果表明，与具有较少层数和/或特征的其他模型相比，所提出的模型显示出更好的统计数据。该模型能够在几分钟内分割数百吉字节的数据，并且可以应用于其他材料和层析成像技术。本文提供了代码和拟合权重，供任何有兴趣的研究人员根据自己的需要使用（https://github.com/manasvupadhyay/erc-gamma-3D-DCNN）。
