  Shape reconstruction of deformable organs from two-dimensional X-ray images
is a key technology for image-guided intervention. In this paper, we propose an
image-to-graph convolutional network (IGCN) for deformable shape reconstruction
from a single-viewpoint projection image. The IGCN learns relationship between
shape/deformation variability and the deep image features based on a
deformation mapping scheme. In experiments targeted to the respiratory motion
of abdominal organs, we confirmed the proposed framework with a regularized
loss function can reconstruct liver shapes from a single digitally
reconstructed radiograph with a mean distance error of 3.6mm.
