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

arXiv: https://arxiv.org/abs/2108.12533 · HTML: https://mlsanity.com/paper/2108.12533/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 28 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [DeepOrganNet: On-the-Fly Reconstruction and Visualization of 3D / 4D Lung Models from Single-View Projections by Deep Deformation Network](https://mlsanity.com/paper/1907.09375/) (2019)
- [Statistical Deformation Reconstruction Using Multi-organ Shape Features for Pancreatic Cancer Localization](https://mlsanity.com/paper/1911.05439/) (2019)

## Built on by (most cited first, 10 of 10)

- [Neural Implicit Surface Reconstruction of Freehand 3D Ultrasound Volume with Geometric Constraints](https://mlsanity.com/paper/2401.05915/) (2024)
- [IGCN: Image-to-graph Convolutional Network for 2D/3D Deformable Registration](https://mlsanity.com/paper/2111.00484/) (2021)
- [From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstruction](https://mlsanity.com/paper/2505.03599/) (2025)
- [MedGS: Gaussian Splatting for Multi-Modal 3D Medical Imaging](https://mlsanity.com/paper/2509.16806/) (2025)
- [2D/3D Deep Image Registration by Learning 3D Displacement Fields for Abdominal Organs](https://mlsanity.com/paper/2212.05445/) (2022)
- [Shape Reconstruction from Thoracoscopic Images using Self-supervised Virtual Learning](https://mlsanity.com/paper/2301.10863/) (2023)
- [Benchmarking Encoder-Decoder Architectures for Biplanar X-ray to 3D Shape Reconstruction](https://mlsanity.com/paper/2309.13587/) (2023)
- [LiverUSRecon: Automatic 3D Reconstruction and Volumetry of the Liver with a Few Partial Ultrasound Scans](https://mlsanity.com/paper/2406.19336/) (2024)
- [Deep-Motion-Net: GNN-based volumetric organ shape reconstruction from single-view 2D projections](https://mlsanity.com/paper/2407.06692/) (2024)
- [Text-to-Image Generation for Projector-Camera System Registration](https://mlsanity.com/paper/2607.03046/) (2026)

## Abstract (arXiv)

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.