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

Autor:	Nakao, M., Tong, F., Nakamura, M., Matsuda, T.
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
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
Externí odkaz:	http://arxiv.org/abs/2108.12533 Zobrazit plný text záznamu View this record from Arxiv