This is an implementation for the experiments described in this article: "A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols" (https://link.springer.com/chapter/10.1007/978-3-030-00928-1_54)

The code has been tested with tensorflow 1.9.0 and python 3.6.6.

For training the initial domains, run 'train_initial_domains.py'. This trains the shared convolutional weights as well as the domain-specific batch normalization weights.

For training on a new domain, first run 'evaluate.py' on the training set of the new domain and identify the closest already learned domain.

Then, initialize the batch normalization parameters of the new domain with those of the closest domain and finetune them with 'train_new_domain.py'. In this training, the convolutional weights are not updated.

Set the paths for your code and data files in 'config/system.py' and set training hyperparameters in 'experiments/unet2D_adaptive_bn.py'.

Several parts of the code were taken from https://github.com/baumgach/acdc_segmenter and modified to suit this application.