About

Interfaces

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

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:¶

• docker (PyTorch, GPU): download
• command line tool (PyTorch, GPU): download
• original code (C++, CPU): download

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