Unpaired lung CT registration¶
Note: Please read the DeepReg Demo Disclaimer.
This is a registration between CT images from different patients. The images are all from acquired at the same timepoint in the breathing cycle. This is an inter subject registration. This kind of registration is useful for determining how one stimulus affects multiple patients. If a drug or invasive procedure is administered to multiple patients, registering the images from different patients can give medical professionals a sense of how each patient is responding in comparison to others. An example of such an application can be seen in .
The dataset for this demo comes from  and can be downloaded from: https://zenodo.org/record/3835682#.XsUWXsBpFhE
Please install DeepReg following the instructions and
change the current directory to the root directory of DeepReg project, i.e.
Please execute the following command to download/pre-process the data and download the pre-trained model. Image intensities are rescaled during pre-processing.
Launch demo training¶
Please execute the following command to launch a demo training. The training logs and
model checkpoints will be saved under
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,
python demos/unpaired_ct_lung/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_ct_lung/logs_predict. Check the CLI documentation
for more details about prediction output.
Optionally, the user-trained model can be used by changing the
demo_predict.py. Note that the path should end with
.ckpt and checkpoints are
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_ct_lung/logs_predict/<time-stamp>/test/<pair-number>/moving_image.nii.gz, demos/unpaired_ct_lung/logs_predict/<time-stamp>/test/<pair-number>/pred_fixed_image.nii.gz, demos/unpaired_ct_lung/logs_predict/<time-stamp>/test/<pair-number>/fixed_image.nii.gz' --slice-inds '40,48,56' -s demos/unpaired_ct_lung/logs_predict
Note: The prediction must be run before running the command to generate the
<pair-number> must be entered by the user.
Please raise an issue for any questions.
 Hering A, Murphy K, and van Ginneken B. (2020). Lean2Reg Challenge: CT Lung Registration - Training Data [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3835682
 Li B, Christensen GE, Hoffman EA, McLennan G, Reinhardt JM. Establishing a normative atlas of the human lung: intersubject warping and registration of volumetric CT images. Acad Radiol. 2003;10(3):255-265. doi:10.1016/s1076-6332(03)80099-5