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Convolutional neural networks (CNNs) have shown promising results on severalsegmentation tasks in magnetic resonance (MR) images. However, the accuracy ofCNNs may degrade severely when segmenting images acquired with differentscanners and/or protocols as compared to the training data, thus limiting theirpractical utility. We address this shortcoming in a lifelong multi-domainlearning setting by treating images acquired with different scanners orprotocols as samples from different, but related domains. Our solution is asingle CNN with shared convolutional filters and domain-specific batchnormalization layers, which can be tuned to new domains with only a few($\approx$ 4) labelled images. Importantly, this is achieved while retainingperformance on the older domains whose training data may no longer beavailable. We evaluate the method for brain structure segmentation in MRimages. Results demonstrate that the proposed method largely closes the gap tothe benchmark, which is training a dedicated CNN for each scanner.

A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols