mlsanityData to 7 Oct 2026

IGCN: Image-to-graph Convolutional Network for 2D/3D Deformable Registration

arXiv 2111.00484 ↗First posted 31 Oct 2021eess.IV cs.CV cs.LGcited by 6

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Abstract (arXiv)

Organ shape reconstruction based on a single-projection image during treatment has wide clinical scope, e.g., in image-guided radiotherapy and surgical guidance. We propose an image-to-graph convolutional network that achieves deformable registration of a 3D organ mesh for a single-viewpoint 2D projection image. This framework enables simultaneous training of two types of transformation: from the 2D projection image to a displacement map, and from the sampled per-vertex feature to a 3D displacement that satisfies the geometrical constraint of the mesh structure. Assuming application to radiation therapy, the 2D/3D deformable registration performance is verified for multiple abdominal organs that have not been targeted to date, i.e., the liver, stomach, duodenum, and kidney, and for pancreatic cancer. The experimental results show shape prediction considering relationships among multiple organs can be used to predict respiratory motion and deformation from digitally reconstructed radiographs with clinically acceptable accuracy.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 16 of its 54 references that are in the map. First posted on arXiv 31 Oct 2021.

Builds on

16 of 54 references.

References · 16

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 972
  4. 2018VoxelMorph: A Learning Framework for Deformable Medical Image Registrationcited by 657
  5. 2020End-to-End Human Pose and Mesh Reconstruction with Transformerscited by 417
  6. 2018A Deep Learning Framework for Unsupervised Affine and Deformable Image Registrationcited by 160
  7. 2019X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networkscited by 85
  8. 2018Learning a Probabilistic Model for Diffeomorphic Registrationcited by 62
  9. 2019Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Networkcited by 62
  10. 2020End-To-End Convolutional Neural Network for 3D Reconstruction of Knee Bones From Bi-Planar X-Ray Imagescited by 28
  11. 2018Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registrationcited by 23
  12. 2017Dilated FCN for Multi-Agent 2D/3D Medical Image Registrationcited by 22
4 more
  1. 2019Multiview 2D/3D Rigid Registration via a Point-Of-Interest Network for Tracking and Triangulation (POINT2)cited by 18
  2. 2019DeepOrganNet: On-the-Fly Reconstruction and Visualization of 3D / 4D Lung Models from Single-View Projections by Deep Deformation Networkcited by 15
  3. 2021Image-to-Graph Convolutional Network for Deformable Shape Reconstruction from a Single Projection Imagecited by 9
  4. 2019Statistical Deformation Reconstruction Using Multi-organ Shape Features for Pancreatic Cancer Localizationcited by 5

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Cited by year

Citing papers per year: 2024: 4; 2025: 2.

6 papers.

  1. 2024A Review on Organ Deformation Modeling Approaches for Reliable Surgical Navigation using Augmented Realitycited by 5
  2. 2024Enhancing medical vision-language contrastive learning via inter-matching relation modellingcited by 4
  3. 2025From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstructioncited by 4
  4. 2024Graph Convolutional Neural Networks for Automated Echocardiography View Recognition: A Holistic Approachcited by 1
  5. 2025PolyPose: Deformable 2D/3D Registration via Polyrigid Transformationscited by 1
  6. 2024Deep-Motion-Net: GNN-based volumetric organ shape reconstruction from single-view 2D projectionscited by 0