A deep learning procedure has been examined for automatic segmentation of 3D tomography images from fiber-reinforced ceramic composites consisting of fibers and matrix of the same material (SiC), and thus identical image intensities. The analysis uses a neural network to distinguish phases from shape and edge information rather than intensity differences. It was used successfully to segment phases in a unidirectional composite that also had a coating with similar image intensity. It was also used to segment matrix cracks generated during in situ tensile loading of the composite and thereby demonstrate the influence of nonuniform fiber distribution on the nature of matrix cracking. By avoiding the need for manual segmentation of thousands of image slices, the procedure overcomes a major impediment to the extraction of quantitative information from such images. The analysis was performed using recently developed software that provides a general framework for executing both training and inference.

中文翻译：

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通过深度学习自动分割纤维增强复合材料的计算机断层扫描图像

已经研究了一种深度学习程序，用于自动分割来自纤维增强陶瓷复合材料的 3D 断层扫描图像，该复合材料由相同材料 (SiC) 的纤维和基质组成，因此图像强度相同。该分析使用神经网络从形状和边缘信息而不是强度差异来区分相位。它成功地用于分割单向复合材料中的相位，该复合材料也具有类似图像强度的涂层。它还用于分割复合材料原位拉伸加载过程中产生的基体裂纹，从而证明不均匀纤维分布对基体裂纹性质的影响。通过避免对数千个图像切片进行手动分割，该程序克服了从此类图像中提取定量信息的主要障碍。该分析是使用最近开发的软件进行的，该软件为执行训练和推理提供了一个通用框架。