IGCN : Image-to-graph convolutional network

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Deep Learning IGCN
Overview

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

IGCN_movie.mp4
  • 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

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Comments
  • About train data

    About train data

    Dear professor, from last year's MIA and MICCAI to this year's TMI, I have the honor to read your research on 3D motion of abdominal organs. Could you provide an example of training data and train_list to help me build a dataset by myself? Thank you very much Looking forward to your reply

    opened by YoungSureshine 2
Owner
Megumi Nakao
Megumi Nakao
GitHub
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