X-Ray radiographs are one of the primary results from inertial confinement
fusion (ICF) experiments. Issues such as scarcity of experimental data, high
levels of noise in the data, lack of ground truth data, and low resolution of
data limit the use of machine/deep learning for automated analysis of
radiographs. In this work we combat these roadblocks to the use of machine
learning by creating a synthetic radiograph dataset resembling experimental
radiographs. Accompanying each synthetic radiograph are corresponding contours
of each capsule shell shape, which enables neural networks to train on the
synthetic data for contour extraction and be applied to the experimental
images. Thus, we train an instance of the convolutional neural network U-Net to
segment the shape of the outer shell capsule using the synthetic dataset, and
we apply this instance of U-Net to a set of radiographs taken at the National
Ignition Facility. We show that the network extracted the outer shell shape of
a small number of capsules as an initial demonstration of deep learning for
automatic contour extraction of ICF images. Future work may include extracting
outer shells from all of the dataset, applying different kinds of neural
networks, and extraction of inner shell contours as well.Comment: 6 pages, 9 figure