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arXiv · 2108.12533

Image-to-Graph Convolutional Network for Deformable Shape Reconstruction from a Single Projection Image

M. Nakao·F. Tong·M. Nakamura·T. Matsuda
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Abstract

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.

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M. Nakao, F. Tong, M. Nakamura, T. Matsuda. 2021-08-28. Image-to-Graph Convolutional Network for Deformable Shape Reconstruction from a Single Projection Image. https://doi.org/10.1007/978-3-030-87202-1_25

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