Unpaired hippocampus MR registration

Note: Please read the DeepReg Demo Disclaimer.

Source Code

Author

DeepReg Development Team (Adrià Casamitjana)

Application

This is a demo targeting the alignment of hippocampal substructures (head and body) using mono-modal MR images between different patients. The images are cropped around those areas and manually annotated. This is a 3D intra-modal registration using a composite loss of image and label similarity.

Data

The dataset for this demo comes from the Learn2Reg MICCAI Challenge (Task 4) [1] and can be downloaded from: https://drive.google.com/uc?export=download&id=1RvJIjG2loU8uGkWzUuGjqVcGQW2RzNYA

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/unpaired_mr_brain/demo_data.py

Pre-processing includes:

  • Rescaling all images’ intensity to 0-255.

  • Creating and applying a binary mask to mask-out the padded values in images.

  • Transforming label volumes using one-hot encoding (only for foreground classes)

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_mr_brain/logs_train.

python demos/unpaired_mr_brain/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/unpaired_mr_brain/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/unpaired_mr_brain/logs_predict. Check the CLI documentation for more details about prediction output.

python demos/unpaired_mr_brain/demo_predict.py

Contact

Please raise an issue for any questions.

Reference

[1] AL Simpson et al., A large annotated medical image dataset for the development and evaluation of segmentation algorithms (2019). https://arxiv.org/abs/1902.09063