基于3D U-Net的增材制造缺陷自动体积分割
Automatic Volumetric Segmentation of Additive Manufacturing Defects with 3D U-Net
摘要：
X射线计算机断层扫描（XCT）图像中的增材制造（AM）缺陷的分割是具有挑战性的，这是由于缺陷的对比度差、尺寸小和外观变化。然而，自动分割可以为增材制造提供质量控制。近年来，三维卷积神经网络（3D CNN）在医学图像的体积分割中表现良好。在这项工作中，我们利用来自医学成像领域的技术，并提出训练3D U-Net模型来自动分割AM样本的XCT图像中的缺陷。这项工作不仅有助于使用机器学习进行AM缺陷检测，还首次展示了AM中的3D体积分割。我们在AM数据集上用3D U-Net的三种变体进行了训练和测试，实现了88.4%的平均交集（IOU）值。
英文摘要：
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%.
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