IGCN : Image-to-graph convolutional network

IGCN is a learning framework for 2D/3D deformable model registration and alignment, and shape reconstruction from a single-viewpoint projection image. The generative network learns translation from the input projection image to a displacement map, and the GCN learns mesh deformation based on the sampled per-vertex feature and connectivity.

Examples

• Left (input): digitally reconstructed radiograph images generated from 4D-CT data (10-frame sequential volumes)
• Center (output): registered mesh of abdominal organs
• Right (error): target (magenta) and predicted (cyan) mesh

Prerequisites

• Python 3.9
• NVIDIA CUDA 11.2.0 and cuDNN 8.1.1
• TFLearn with Tensorflow backend

Reference

If you use this code for your research, please cite

• IGCN (The latest version):
  M. Nakao, M. Nakamura, T. Matsuda, IGCN: Image-to-graph Convolutional Network for 2D/3D Deformable Registration, arXiv, 2111.00484, 2021. https://arxiv.org/abs/2111.00484

• IGCN Warp (MICCAI version):
  M. Nakao, M. Nakamura, T. Matsuda, Image-to-Graph Convolutional Network for Deformable Shape Reconstruction from a Single Projection Image, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 259-268, 2021. https://link.springer.com/chapter/10.1007/978-3-030-87202-1_25