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.
@article{arxiv.2108.12533,
title = {Image-to-Graph Convolutional Network for Deformable Shape Reconstruction from a Single Projection Image},
author = {M. Nakao and F. Tong and M. Nakamura and T. Matsuda},
journal= {arXiv preprint arXiv:2108.12533},
year = {2021}
}