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Title: Image-to-Graph Convolutional Network for Deformable Shape Reconstruction from a Single Projection Image
Authors: Nakao, Megumi  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0002-5508-4366 (unconfirmed)
Tong, Fei
Nakamura, Mitsuhiro
Matsuda, Tetsuya
Author's alias: 中尾, 恵
中村, 光宏
松田, 哲也
Keywords: Graph convolutional network
Shape reconstruction
Respiratory motion
X-ray image
Issue Date: Sep-2021
Publisher: Springer International Publishing
Journal title: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021
Start page: 259
End page: 268
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 3.6 mm.
Description: 24th International Conference, Strasbourg, France, September 27–October 1, 2021
Part of the Lecture Notes in Computer Science book series (LNIP, volume 12904)
Rights: This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-030-87202-1_25
The full-text file will be made open to the public on 21 September 2022 in accordance with publisher's 'Terms and Conditions for Self-Archiving'.
This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。
URI: http://hdl.handle.net/2433/270019
DOI(Published Version): 10.1007/978-3-030-87202-1_25
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