Classical affine registration for head-and-neck CT images

Note: Please read the DeepReg Demo Disclaimer.

Source Code

This is a special demo that uses the DeepReg package for classical affine image registration, which iteratively solves an optimisation problem. Gradient descent is used to minimise the image dissimilarity function of a given pair of moving anf fixed images.

Author

DeepReg Development Team

Application

Although in this demo the moving images are simulated using a randomly generated transformation. The registration technique can be used in radiotherapy to compensate the difference between CT acquired at different time points, such as pre-treatment and intra-/post-treatment.

Instruction

  • Change current directory to the root directory of DeepReg project;

  • Run demo_data.py script to download an example CT volumes with two labels;

python demos/classical_ct_headneck_affine/demo_data.py

  • Run demo_register.py script. This script will register two images. The fixed image will be the downloaded data and the moving image will be simulated by applying a random affine transformation, such that the ground-truth is available for. The optimised transformation will be applied to the moving images, as well as the moving labels. The results, saved in a timestamped folder under the project directory, will compare the warped image/labels with the ground-truth image/labels.

python demos/classical_ct_headneck_affine/demo_register.py

Tested DeepReg version

0.1.0

Contact

Please raise an issue.

Reference

[1] Vallières, M. et al. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Sci Rep 7, 10117 (2017). doi: 10.1038/s41598-017-10371-5