Segmentation of additive manufacturing (AM) defects in X-ray Computed Tomography (XCT) images is challenging, due to the poor contrast, small sizes and variation in appearance of defects. Automatic segmentation can, however, provide quality control for additive manufacturing. Over recent years, three-dimensional convolutional neural networks (3D CNNs) have performed well in the volumetric segmentation of medical images. In this work, we leverage techniques from the medical imaging domain and propose training a 3D U-Net model to automatically segment defects in XCT images of AM samples. This work not only contributes to the use of machine learning for AM defect detection but also demonstrates for the first time 3D volumetric segmentation in AM. We train and test with three variants of the 3D U-Net on an AM dataset, achieving a mean intersection of union (IOU) value of 88.4%.

翻译：在X射线成像(XCT)图像中,添加剂制造缺陷的分解具有挑战性,因为对比差、体积小和外形变异,自动分解能为添加剂制造提供质量控制,近年来,三维进化神经网络(3D CNNs)在医疗图像的体积分解方面表现良好。在这项工作中,我们利用医学成像领域的技术,并提议培训3D U-Net模型,使AM样本的XCT图像自动产生部位缺陷。这项工作不仅有助于利用机器学习来检测AM缺陷,而且首次展示AM的3D体积分解。我们在AM数据集上用3DU-Net的三个变体进行培训和测试,达到88.4%的平均值组合的交叉值。