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

• URL: http://arxiv.org/abs/2108.12533v2
• Date: Tue, 31 Aug 2021 09:50:27 GMT
• Title: Image-to-Graph Convolutional Network for Deformable Shape Reconstruction from a Single Projection Image
• Authors: M. Nakao, F. Tong, M. Nakamura, T. Matsuda
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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