Unpaired lung CT registration

Note: Please read the DeepReg Demo Disclaimer.

Source Code

Author

DeepReg Development Team (Shaheer Saeed)

Application

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 [2].

Data

The dataset for this demo comes from [1] and can be downloaded from: https://zenodo.org/record/3835682#.XsUWXsBpFhE

Instruction

Installation

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/unpaired_ct_lung/demo_data.py

Launch demo training

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

python demos/unpaired_ct_lung/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_ct_lung/demo_train.py --full

Predict

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.

python demos/unpaired_ct_lung/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.

Visualise

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 visualisation. The <time-stamp> and <pair-number> must be entered by the user.

plot

Contact

Please raise an issue for any questions.

Reference

[1] 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

[2] 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