Unpaired prostate ultrasound registration

Note: Please read the DeepReg Demo Disclaimer.

Source Code

This DeepReg Demo is also an example of cross validation.


DeepReg Development Team


Transrectal ultrasound (TRUS) images are acquired from prostate cancer patients during image-guided procedures. Pairwise registration between these 3D images may be useful for intraoperative motion modelling and group-wise registration for population studies.


The 3D ultrasound images used in this demo were derived from the Prostate-MRI-US-Biopsy dataset, hosted at the Cancer Imaging Archive (TCIA).



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. Data are split into 10 folds for cross-validation.

python demos/unpaired_us_prostate_cv/demo_data.py

Launch demo training

Please execute the following command to launch a demo training (the first of the ten runs of a 9-fold cross-validation). The training logs and model checkpoints will be saved under demos/unpaired_us_prostate_cv/logs_train.

python demos/unpaired_us_prostate_cv/demo_train.py

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/unpaired_us_prostate_cv/demo_train.py --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/unpaired_us_prostate_cv/logs_predict. Check the CLI documentation for more details about prediction output.

python demos/unpaired_us_prostate_cv/demo_predict.py

Optionally, the user-trained model can be used by changing the ckpt_path variable inside demo_predict.py. 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/unpaired_us_prostate_cv/logs_predict/<time-stamp>/test/<pair-number>/moving_image.nii.gz, demos/unpaired_us_prostate_cv/logs_predict/<time-stamp>/test/<pair-number>/pred_fixed_image.nii.gz, demos/unpaired_us_prostate_cv/logs_predict/<time-stamp>/test/<pair-number>/fixed_image.nii.gz' --slice-inds '50,65,35' -s demos/unpaired_us_prostate_cv/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.