Image-to-Graph Convolutional Network for Deformable Shape Reconstruction from a Single Projection Image
Autor: | Nakao, M., Tong, F., Nakamura, M., Matsuda, T. |
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Rok vydání: | 2021 |
Předmět: | |
Zdroj: | International Conference on Medical Image Computing and Computer Assisted Intervention 2021 (MICCAI) |
Druh dokumentu: | Working Paper |
DOI: | 10.1007/978-3-030-87202-1_25 |
Popis: | 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. Comment: This paper will be appeared in MICCAI 2021 |
Databáze: | arXiv |
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