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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2108.12533 (eess)
[Submitted on 28 Aug 2021 (v1), last revised 31 Aug 2021 (this version, v2)]

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

Authors: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.
Comments: This paper will be appeared in MICCAI 2021
Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2108.12533 [eess.IV]
  (or arXiv:2108.12533v2 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2108.12533
Journal reference: International Conference on Medical Image Computing and Computer Assisted Intervention 2021 (MICCAI)
Related DOI: https://doi.org/10.1007/978-3-030-87202-1_25

Submission history

From: Megumi Nakao [view email]
[v1] Sat, 28 Aug 2021 00:00:09 UTC (2,091 KB)
[v2] Tue, 31 Aug 2021 09:50:27 UTC (2,091 KB)
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