Paired lung CT registration

Note: Please read the DeepReg Demo Disclaimer.

Source Code


DeepReg Development Team (Shaheer Saeed)


This is a registration between CT images acquired at different time points for a single patient. The images being registered are taken at inspiration and expiration for each subject. This is an intra subject registration. This type of intra subject registration is useful when there is a need to track certain features on a medical image such as tumor location when conducting invasive procedures.


The dataset for this demo comes from Lean2Reg Challenge: CT Lung Registration - Training Data [1].



Please install DeepReg following the instructions and change the current directory to the root directory of DeepReg project, i.e. DeepReg/.

Download data

Please execute the following command to download/pre-process the data and download the pre-trained model. Image intensities are rescaled during pre-processing.

python demos/paired_ct_lung/

Launch demo training

Please execute the following command to launch a demo training. The training logs and model checkpoints will be saved under demos/paired_ct_lung/logs_train.

python demos/paired_ct_lung/

Here the training is launched using the GPU of index 0 with a limited number of steps and reduced size. Please add flag --full to use the original training configuration, such as

python demos/paired_ct_lung/ --full


Please execute the following command to run the prediction with pre-trained model. The prediction logs and visualization results will be saved under demos/paired_ct_lung/logs_predict. Check the CLI documentation for more details about prediction output.

python demos/paired_ct_lung/

Optionally, the user-trained model can be used by changing the ckpt_path variable inside Note that the path should end with .ckpt and checkpoints are saved under logs_train as mentioned above.


The following command can be executed to generate a plot of three image slices from the the moving image, warped image and fixed image (left to right) to visualise the registration. Please see the visualisation tool docs here for more visualisation options such as animated gifs.

deepreg_vis -m 2 -i 'demos/paired_ct_lung/logs_predict/<time-stamp>/test/<pair-number>/moving_image.nii.gz, demos/paired_ct_lung/logs_predict/<time-stamp>/test/<pair-number>/pred_fixed_image.nii.gz, demos/paired_ct_lung/logs_predict/<time-stamp>/test/<pair-number>/fixed_image.nii.gz' --slice-inds '64,50,72' -s demos/paired_ct_lung/logs_predict/

Note: The prediction must be run before running the command to generate the visualisation. The <time-stamp> and <pair-number> must be entered by the user.



Please raise an issue for any questions.


[1] Hering, Alessa, Murphy,Keelin, and van Ginneken, Bram. (2020). Lean2Reg Challenge: CT Lung Registration : CT Lung Registration - Training Data