Pairwise registration for grouped prostate segmentation masks¶
Note: Please read the DeepReg Demo Disclaimer.
This demo uses DeepReg to demonstrate a number of features:
For grouped data in h5 files, e.g. “group-1-2” indicates the 2th visit from Subject 1;
Use masks as the images for feature-based registration - aligning the prostate gland segmentation in this case - with deep learning;
Register intra-patient longitudinal data.
Author¶
DeepReg Development Team
Application¶
Longitudinal registration detects the temporal changes and normalises the spatial difference between images acquired at different time-points. For prostate cancer patients under active surveillance programmes, quantifying these changes is useful for detecting and monitoring potential cancerous regions.
Data¶
This is a demo without real clinical data due to regulatory restrictions. The MR and ultrasound images used are simulated dummy 3D Ultrasound images. Data are organized into 10 separate folds.
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.
python demos/grouped_mask_prostate_longitudinal/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/grouped_mask_prostate_longitudinal/logs_train
.
python demos/grouped_mask_prostate_longitudinal/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 --no-test
to use the original training
configuration, such as
python demos/grouped_mask_prostate_longitudinal/demo_train.py --no-test
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/grouped_mask_prostate_longitudinal/logs_predict
. Check the
CLI documentation for more details about prediction output.
python demos/grouped_mask_prostate_longitudinal/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.
Contact¶
Please raise an issue for any questions.