corrField


Logo for corrField

About

Image Version:
20fbff09-2e33-48a7-ba61-83f084ff2ea1
Last updated:
May 3, 2021, 8:08 a.m.

Interfaces

This algorithm implements all of the following input-output combinations:

Inputs Outputs
1
  • Fixed Image (Image)
  • Moving Image (Image)
  • Fixed Mask (Segmentation)
  • corrField Configuration (Anything)
  • Results JSON File (Anything)
  • Warped Image (Image)
  • Displacement Field (Image)
  • Model Facts

    Summary

    Correspondence fields for large motion image registration. This algorithm is an extended PyTorch (GPU support) implementation of the corrField method described in [1] and was developed as part of the work for [2].

    Source code:ΒΆ

    For further questions or comments please contact us at {hansen,heinrich}@imi.uni-luebeck.de.

    [1] Heinrich, Mattias P., Heinz Handels, and Ivor JA Simpson. "Estimating large lung motion in COPD patients by symmetric regularised correspondence fields." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2015.
    
    [2] Hansen, Lasse, and Mattias P. Heinrich. "GraphRegNet: Deep Graph Regularisation Networks on Sparse Keypoints for Dense Registration of 3D Lung CTs." IEEE Transactions on Medical Imaging (2021).
    

    Mechanism

    Validation and Performance

    Uses and Directions

    This algorithm was developed for research purposes only.

    Warnings

    Common Error Messages

    Information on this algorithm has been provided by the Algorithm Editors, following the Model Facts labels guidelines from Sendak, M.P., Gao, M., Brajer, N. et al. Presenting machine learning model information to clinical end users with model facts labels. npj Digit. Med. 3, 41 (2020). 10.1038/s41746-020-0253-3