Pairwise registration for grouped prostate segmentation masks

Note: Please read the DeepReg Demo Disclaimer.

Source Code

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 images.

Instruction

  • Install DeepReg;

  • Change current directory to the root directory of DeepReg project;

  • Run demo_data.py script to download 10 folds of unpaired 3D ultrasound images and the pre-trained model.

python demos/grouped_mask_prostate_longitudinal/demo_data.py

  • Call deepreg_train from command line. The following example uses a single GPU and launches the first of the ten runs of a 9-fold cross-validation, as specified in the ``dataset` section <./grouped_mask_prostate_longitudinal_dataset0.yaml>`_ and the ``train` section <./grouped_mask_prostate_longitudinal_train.yaml>`_, which can be specified in separate yaml files;

deepreg_train --gpu "0" --config_path demos/grouped_mask_prostate_longitudinal/grouped_mask_prostate_longitudinal.yaml --log_dir grouped_mask_prostate_longitudinal

  • Call deepreg_predict from command line to use the saved ckpt file for testing on the data partitions specified in the config file, a copy of which would be saved in the [log_dir]. The following example uses a pre-trained model, on CPU. If not specified, the results will be saved at the created timestamp-named directories under /logs.

deepreg_predict --gpu "" --config_path demos/grouped_mask_prostate_longitudinal/grouped_mask_prostate_longitudinal.yaml --ckpt_path demos/grouped_mask_prostate_longitudinal/dataset/pre-trained/weights-epoch500.ckpt --save_png --mode test

Pre-trained model

A pre-trained model will be downloaded after running demo_data.py and unzipped at the dataset folder under the demo folder. This pre-trained model will be used by default with deepreg_predict. Run the user-trained model by specifying with --ckpt_path the location where the ckpt files will be saved, in this case (specified by deepreg_train as above), /logs/grouped_mask_prostate_longitudinal/.

Tested DeepReg version

Last commit at which demo was tested: 3157f880eb99ce10fc3a4a8ebcc595bd67be24e1

Contact

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