  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%.
